全球降水観測計画（GPM）における二周波降水レーダ観測への期待（TRMMからGPMへ）
井口俊夫（情報通信研究機構、電磁波計測研究所）
with help of many TRMM and GPM scientists, in particular,
Christian Kummerow (Colorado State U.)
Yukari Takayabu (U of Tokyo)
Takuji Kubota (JAXA)
安全安心ICTフォーラム
October 24, 2014
Fukuoka University, Fukuoka, Japan

Rainfall Measurement and Our Life
Rain affects most everyone's life & work
– Food production– Flood, drought
Rain is a key variable in– Weather prediction models– Climate models– Air-sea interaction models, etc.
Rain is one of hardest meteorological parameters
to measure, because of its large spatial andtemporal variability.
– Contribution by rainfall measuring satellites
TRMM (Tropical Rainfall Measuring Mission)
GPM (Global Precipitation Measurement)Precipitation as a Heat Engine
Convection
Convection
Convection

Condensation

Heating

Latentheat

Heating

Evaporation

Cooling

CoolingAtmospheric Circulation and Tropical Rainfall
Normal Condition

El Nino Condition

NorthNorth

Equator赤道

北Equator
赤道

Warm water

Maritime
大洋州
Continent

暖かい 水
Cold water
冷 たい水
W

E

北

Warmwater
暖かい水
Cold water
South南米
America

Maritime
大洋州
Continent

冷 たい水

WESouth南米
America• Tropical Rainfall as a “Heat Engine” of global circulation

TRMM Objectives - Metrics
 To obtain and study multi-year science data sets of tropical andsubtropical rainfall measurements:
 Launch and successfully operate satellite and data system for 3 years.
 To understand how interactions between the sea, air, and land masses
produce changes in global rainfall and climate: Produce accurate maps (10%) of global precipitation at 5°x5°monthly resolution for at least 3 years. Calibrate previous rainfall
estimates to extend time series.
 To help improve modeling of tropical rainfall processes and theirinfluence on global circulation in order to predict rainfall andvariability at various time scale intervals:
 Derive Latent Heating products in the tropics; Map Diurnal Cycle ofPrecipitation; Make data available to modeling community; To test, evaluate, and improve the performance of satellite rainfall
estimate measurements and techniques: Operate a quantitative validation program and reprocess data aswarranted.

TRMM and Precipitation Radar
TMI

TRMM Microwave
Imager

VIRS
LIS

Lightning
Imaging Sensor

CERES

Clouds and Earth’s
Radiant Energy SystemVisible Infrared
Scanner

2mPR

Precipitation
Radar

TRMM PRFirst spaceborne precipitation radar
Developed by NICT and JAXA, JAPANKu-band radar 13.8GHz: moderate toheavy rain
TRMM Orbit
35N-35S, non-sunsynchronized•••

Concept of TRMM Rain Observation
FlightSpeed:
7.3 km/sec
飛行速度: 7.3 km/sec

PR: Precipitation Radar
PR: 降雨レーダ

TMI: TRMM Microwave Imager
TMI: TRMMマイクロ波観測装置

VIRS:
Visible/IR Scanner
VIRS: 可視赤外観測装置

PR
Mass at launch: 3.5 tonDimensions:
5.1 m ｘ 3.0 m ｘ 3.5 m
Solar paddles (2):2.1 m ｘ 4.3 m each

VIRS

TMI

Orbit inclination：35 deg.
Period：91.52 min
non-sun-synchronous orbit
350 km

250 m

760 km215 km

720 km6～ 50km

4km
2km

Principles of Precipitation Measurementwith Satellite-borne Sensors
衛星搭載センサによる降水測定の原理

Thirteen-year (1998-2010) TRMM
Composite Climatology (TCC)
Climatological value is meanof three input products ateach 0.5 lat./long. grid from
Adler/Wang

Standard deviation (σ)
among the three products
is an estimate of error—
note E. Pac. peak values(σ/mean= 1.3/9 = 14%)

Agreement between PR and TMI Rain Estimates

Latitudesolid-line: V7
dashed-line: V6

red: PR
blue: TMI
V4

Precipitation Rate(mm/month)Latent Heating by Convection, 98-07 June-Aug
Latent heating at 7.5km deep convection

Latent heating at 2.0km cumulus congestus

Latent heating by convection is estimated utilizing TRMM PR (Shige et al. 2004).
Significant distribution of cumulus congestus regime emerged out over moderately warm
water with large-scale atmospheric subsidence. (Takayabu et al. 2010)

Hurricane Structure and Variations Studied with TRMM

12Diurnal Variation of Rainfall(Local Time of Max. Rainfall）

Significant Contract between Land and the Oceans
0.1 degree 1998-2010

Courtesy of Masafumi Hirose

Rainfall Profiles in Bangladesh

Kenji Nakamura

10-year mean (92°E)

B• Apr-May: Pre-Monsoon
Deep and Intense Rainfall
Large effects of small topography• Jun-Aug: Monsoon Rainy Season
Moderate and Uniform Rainfall
Apr.-MayA

Jun-Aug

mm/h

km

mm/h

Chitagong

Akm

MeghalayaBA

Chitagong

MeghalayaBA Contrast in Precipitation Across the Baiu Front
1412108642

Meridional-vertical structure (122-145E)
June-July, 1998-2011
Color : Moist static energy CpT+gz+Lq

Convective
0.9
0.7
0.5
0.3
0.1

Stratiform
14128105.686442.4210 7.5 5 2.5 0 -2.5 -5 -7.5 -10 0.8
Relative Latitude[*e-3]

Subtropicaljet

N

θe[K]

SJun-Jul 98-11 122-145E

Baiu Front
Yokoyama et al. 2013, submitted

Pressure (hPa)

TRMM SLH (Q1-QR)
Conv. Diabatic Heating of the Atmosphere1998-2005 Sep.-Nov. 10S long-height section

DRY(Takayabu et al. 2010, modified)Extreme Rainfall vs. Extreme Convection
Top 0.1% in each 2.0 deg x 2.0 deg
grid in terms of surface rain rate

Top 0.1% in terms of40dBZ echo top height

Profiles of extremely intense rainfall and extremely tall convection are very different
Hamada et al. 2014 in prep.

Regional Characteristics of R/H-only Extreme Profiles
Amazon- Same irrespective of regions- Extreme rain rate is mostly
associated with moderate
systems, even in the regions
where severe convective
storms are dominant

Eq. Africa

Eq. W. PacificUS

JP (Ocean)18Impact of Asian Pollution on Rainfall?
Importance of Radar and Passive Microwave on Same Satellite

Remaining Differences
between Passive
Microwave and Radar
Retrievals --an extreme
exampleTMI shows significant
rain area, but PR shows
very little
Result probably due toaerosol effects (pollution)
on size distribution ofcloud/rainfall droplets(high cloud liquid water content, but
small amount of rain)

Berg and Kummerow

19

The TRMM Precipitation Feature Database• Continuously updated database ofprecip. features• Easy subsetting by location, time,feature size, dBZ, or otherparameters• Identifies occurrences ofextremes, rain feature population
statistics, vertical structure

Occurrence of 20 dBZ fordifferent heights

Global Satellite Mapping of Precipitation (GSMaP) :A prototype for GPM

GSMaP• Merged global rainfall map fromTRMM, AMSR2 and othermicrowave sensors• Hourly, 0.1-degree lat/lon• Available 4-hr after observation• Browse images, 24-hr animation,
displaying by Google Earth• Data are also available via
password protected ftp site

Distribution

http://sharaku.eorc.jaxa.jp/GSMaPImprovement of GMaP:
from TRMM Era to GPM Era
TRMM eraDB from a model

DB fromTRMM/PR data

DB from a model

GPM eraDB from GPM/DPRdata

Improve accuracy of
MWR estimates at all
latitudes(Plot by M. Kachi)

22

Observation by a Fleet of Satellites with
Microwave Radiometer
Observation area with
MWRs in 3 hours(1, 2, 4 and 8 satellites fromtop to bottom)

Coverages byTRMM PR andGPM DPR in a day

１２４８

GPM

(Global Precipitation
Measurement Mission)

DPR(Dual-frequency
Precipitation Radar)

(1)

(1)

GPM coresatellite

azimuth

KaPR:
35.5GHz

KuPR:
13.6GHz

Altitude
407km(24)(25)

Spaceborne Atmospheric Radars
TRMM/PR – NICT/JAXA
Ku, Scanning , Tropical Rain

GPM/DPR – NICT/JAXA
Ku/Ka, Scanning, Precipitation

ACE Radar (one concept)
W/Ka, Scanning, Doppler

1997-Today
CloudSat/CPR – JPL/NASAW, -30dBZ , Clouds
EarthCARE/CPR – NICT/JAXA
W, Doppler, Clouds

NASA/JAXA worshop on ACE Mission –Lihue July 29-31 2008

2006 -Today

TRMM’s Achievements
TRMM/PR has opened a new era of accurate precipitation measurement fromspace.
• Demonstrated the world’s first space-borne precipitation radar technology• Improved the accuracy of precipitation estimates from space and made spacestandard for measuring precipitation• Improved climatology of tropical rainfall and variations from 16-year
accumulation of 3D rainfall data, multi-sensor measurements, diurnal samplings.
• Vertical hydrometeor/heating structure and diurnal signal• Became a new standard precipitation data set for numerical model evaluations
and improved climate and weather modeling• Multi-satellite (~3-hr) rainfall analyses using TRMM+other satellites (GSMaP)• Operational use of data by weather agencies with 4-D data assimilation.
Other studies include• Impact of humans on precipitation• Hurricane/typhoon structure/evolution• Flood and agricultural applications
More than 3000 scientific journal articles
published using TRMM data
Successful cooperation between US and Japan

Summary• TRMM started as a science mission.
– It has achieved the original science objectives and much
more.
• Improved the MWR algorithm significantly.

– Several practical applications have emerged.
• Weather forecast, Flood warning, etc.

• GPM inherits TRMM’s success.
– GPM instruments will set a new standard for precipitation
measurements from space– The GPM mission will help advance our understanding ofEarth's water and energy cycles, improve the forecastingof extreme events that cause natural disasters, andextend current capabilities of using satellite precipitation
information to directly benefit society.