Basics of precipitation measurement from space
Kenji Nakamura
Hydrospheric Atmospheric Research Center
Nagoya University

International Hydrological Programme
Precipitation Measurement from Space and its Applications
The Twenty-second IHP Training Course
18 November - 1 December, 2012

1957: First satellite Sputok
1960-65: TIROS-1, -10(Television and Inra-Red Observation Satellite)
120-140 kg
1966-69: ESSA-1, -9
Operated by Environmental Science Service Administration
 NOAA
1970-: NOAA series
From TIROS-1 in 1960

(composite image of clouds for 13 Feb. 1965 from TIROS-9 ( Earth Observations from Space, 2008)

Applications Technology Satellite (ATS) (NASA)
ATS-1: Launch: 7 Dec, 1966.
Geostationary orbit
Spin stabilized
Dry weight: 352 kg, Length: 1.35 m
Communication text instruments
Spin scan cloud camera: every 30 minutes
Visible 1 channel
http://science.nasa.gov/missions/ats/
Pixel: 4.6 km at nadir
ATS-3: Nov. 1967
Visible 3 channels

From ATS-3 in 1970

1964-1978: NIMBUS series (NASA)
Less than one month
Visible/infrared channels
1972: Nimbus-5, 10 years,
Electrically scanning Microwave Radiometer(ESMR) (1.55 cm wavelength)
SST, snow cover, sea ice, first/multi-year ice,
rainfall,etc.

1978: Nimbus-7, > 9 years
Scanning Multichannel Microwave Radiometer(SMMR)
0.8, 1.4, 1.7, 2.7, and 4.6 cm (H/V)

Arctic sea ice distribution for 1980-2000 from microwave radiometers
1980

2000
(NASA)

SEASAT

Launched in 1978 by NASA
Operated for 105 days
Orbit: Altitude: 800 km,
Inclination: 108 degrees
Active microwave sensors:
Altimeter: spacecraft height fromocean surface
Sscatterometer: ocean surface wind
Synthetic aperture radar (SAR):
ocean surface
SMMR: Sea surface temperature
VIS/IR radiometer:cloud, land, water features

Earth observation from space
Remote sensing using optical waves or microwaves
Transparency of atmosphere
Exceptions: gravity mission, occultation technique

Orbit of satellite
Low Earth Orbit (LEO).
Altitude: 350-1000 km
Generally, polar orbit
Exceptions: TRMM, Megha-TropiqueSensors: passive/active, optical/microwave
Geostationary Earth Orbit (GEO)
Altitude: 36,000 km
Above equator
Exception:
Sensors: optical, passive

Sensor aperture: DθHeight: HθPath length
difference δ = θ D/2

Beam width in radian: θNearly parallel

Pixel size: L

If δ = λ/2,
cancellation occurs.

Beam width θ = c x λ / D(c: order of 1)
Pixel size on the Earth’s surface = c x Height x θ

Example:Microwave of 10 GHz (3 x 10 9 s-1)
Lambda = 300 M m s-1 / 10 GHz = 3 cm
Antenna size of 1 m
Satellite Height of 1000 km
 Pixel size = c x 3 cm / 1 m x 1000 km = c x 30 km
Satellite Height of 36,000 km
 Pixel size = c x 1000 km
Similarly for optical wave
Optical wave of 1 micrometer (1 x 10 -6 m)
Aperture diameter of 10 cm
 Pixel size = c x 1 micrometer / 10 cm x 36000 km = c x 360 m

Satellite Orbit

LEO: altitude:350-1,000 km
Period: 90 min.

GPS: 20,200 km

Eclipse on 21 May
2012 (from JAXA
website)GEO: 35,800 km
Period: 24 hours

20
deg

ISS at about 400 km altitude(from JAXA website )Instantaneous observation
Quasi-continuous observation

Eclipse by Earth.
Thin atmosphere（http://www.jaxa.jp)

Aurola at 100-150km height. Green light is from atomic oxygen.

Newton’s law
GMm/r2 = mv2/r
And
GM/re2 = g, re = 6,400 km, g = 10 m s2,
Thenv = re root (g/r)
For e
Example: near Earth’s surface: v = 8 km/s
Period:T = 2πr / v
Then
T = 2π (r/re) root (r/g)
Example:Near Earth’s surface: T = 90 min.
r = 36,000 km: T = 24 hours

Non-sun synchronous orbit

Deviation of gravity fieldfrom sphere coupled forces.

Sun synchronous orbit

Coupled forces rotate
the orbit plane.
Gyro

Centrifugal force

Centrifugal force

Rotation of the orbit plane:-0.58 deg. (Re/r)2 x cos (i) / revolution
Solar movement:
360 degrees / year
For low orbit, 16 revolution / day Roughly 1 degree / day cos (i) = 0.11  i = 97 degrees

Inclination i

v

v

v

Three wheels for
atitude controlv

GCOM-W1 Specifications
SenosrAdvanced Microwave radiometer2（AMSR2）

Designed life time

5 years

LaunchMay 2012 H-IIA rocket

Weight1910 kg

Size

With two deployable solar paddles
5.1 m (X)×17.5 m (Y)×3.4 m (Z)Orbit

Sun synchromous revisiting

Altitude699.6km (over equator)

Inclination

98.186 degs.

Ascending node local time

13:30 ± 15 min.

Non-sun synchronous orbit

Deviation of gravity fieldfrom sphere coupled forces.

Sun synchronous orbit

Coupled forces rotate
the orbit plane.
Gyro

Centrifugal force

Centrifugal force

Rotation of the orbit plane:-0.58 deg. (Re/r)2 x cos (i) / revolution
Solar movement:
360 degrees / year
For low orbit, 16 revolution / day Roughly 1 degree / day cos (i) = 0.11  i = 97 degrees

Inclination i

v

v

v

Three wheels for
atitude controlv

GCOM-W1主要諸元
項目

仕様

ミッション
機器

高性能マイクロ波放射計2（AMSR2）

設計寿命

5年

打ち上げ予定2012年1月 H-IIAロケット

質量

1910kg

衛星形状

2翼太陽電池パドルを有する
5.1m (X)×17.5m (Y)×3.4m (Z)(軌道上展開
形状)

軌道種別

太陽同期準回帰軌道

軌道高度699.6km (赤道上)

軌道傾斜角98.186度

昇交点通過
地方太陽時

13時30分 ±15分

v

v

v

Three wheels for
atitude controlv

GCOM-W1 Specifications
SenosrAdvanced Microwave radiometer2（AMSR2）

Designed life time

5 years

LaunchMay 2012 H-IIA rocket

Weight1910 kg

Size

With two deployable solar paddles
5.1 m (X)×17.5 m (Y)×3.4 m (Z)Orbit

Sun synchromous revisiting

Altitude699.6km (over equator)

Inclination

98.186 degs.

Ascending node local time

13:30 ± 15 min.

23

Rocketm: fuel consumption(M-m)v = ((M-m) – dm)(v + dv) + (v – w) dm, w: velocity of gas(M-m) v

(M - m – dm) (v + dv)
dm (v - w)
d v = w dm /(M - m)
 v-v0 = w loge [m0/(m0-mf)](Tsiolkovskii’s formula)W (H2/O2): 4300 m/s (Isp (specific impulse) = w/g = 440 sec)
m0 = (30t) exp [ (8 km/s) / (4300 m/s)] = 180t
24

Multi-Stage Rocket
M= Mf + Mp

M=M1+M2 , Mf=M1f+M2f

M1=M1f + M2

M2=Mp + M2f

Mf: weight of fuel,Mp: weight of payloadv = w loge [(M/(M-Mf)]= w loge (M/Mp)

v1 = w loge (M/M2)
v2 = v1 + w loge (M2/M2p)= w [ loge (M/M2) + loge (M2/Mp) ]= w loge (M/Mp)

Multi-Stage Rocket
M=M1+M2 , Mf=M1f+M2f

M1=M1f + M2
Mf: weight of fuel,Ms: weight of structure,
Mp: weight of payload

M=M1+M2

M2=M2s + Mp + M2f
M1=M1f + (M1s + M2)

v = w loge [(M/(M1s+M2s+Mp)]
v1 = w loge (M/(M1s + M2))
v2 = v1 + w loge (M2/(M2s + Mp))
= w loge [M/(M1s + M2) x (M2/(M2s+Mp) ]V2/V = w loge [(M1s+M2s+Mp) / [M1s (M2s+Mp)/M2 +M2s+Mp] ]> 1

Multi-Stage Rocket
M=M1+M2

Second stage engine

M2=M2s + Mp + M2f + M2e
M1=M1f + (M1s + M2)
v1 = w loge (M/(M1s + M2))
v2 = v1 + w loge (M2/(M2s + Mp+ M2e))= w loge [M/(M1s + M2) x (M2/(M2s+Mp+ M2e) ]V2/V = w loge [(M1s+M2s+Mp) / [M1s (M2s+Mp+ M2e)/M2 +M2s+Mp+
M2e] ] > 1

GOES Precipitation Index (GPI)
Original: Arkin andMeisner, 1987, Mon. Wea. Rev., 115, 51-74.
Rain (mm) = Frac x 3 mm/hour
where
Frac: fractional coverage of GOES image with < 235 K
at around 11-12 micron.
Adjusted GPI (AGPI)
Incorporation of regional and seasonal biases.
Reference
Adler, R. F., G. J. Huffman, and P. R. Keehn 1994: Global rain estimates frommicrowave-adjusted geosynchronous IR data, Remote Sens. Rev., 11, 125-152.

GCOM-W1/AMSR2: a successor of AMSR-E• GCOM-W1 (JFY 2011~) will carry microwave imager: AMSR2– Deployable main reflector system with 2.0m diameter• Achieve 20% finer resolution than AMSR-E with 1.6m reflector– Frequency channel set is identical to that of AMSR-E except 7.3GHz channel for Radio
Frequency Interference mitigation– Two-point external calibration with the improved HTS (hot-load)– Add a redundant momentum wheel to increase reliability• GCOM-W1 in the A-Train– GCOM-W1 will join the A-Train constellation– Participating in the A-Train will benefit– Precise inter-calibration between AMSR-E and AMSR2– Synergy with the other A-Train instruments for new Earth science researchTropical Rainfall Measuring Mission: TRMM
Solar paddle

High-gain antenna
TMI

VIRS
LIS

CERES

PR

Observation of tropicalrainfall (Driving engine ofglobal atmosphere)
US-Japan joint mission(Japan: PR, Launch,
US: Bus, 4 sensors,operation)
Launched in Nov., 1997.
Still under operation

Orbit
Altitude
Inclination

Circular（Non-Sun Synchronous）
350km (402.5km since Aug. 2001) (±1.25km)
35 deg.

SensorPrecipitation Radar (PR)TRMM Microwave Imager (TMI)
Visible and Infrared Scanner (VIRS)
Clouds and the Earth’s Radiation Energy System (CERES)

Lightning (LIS)First space-borne
precipitation radardeveloped by CRL and
NASDA
COMMUNICATIONSRESEARCHLABORATORY

30

Microwave radiometer (Examples of TRMM)
Overpass

31

I0
T

I = I0 – dI = I0 – a drdI’ = e dr

Local equilibrium  amount of absorption = amount emission
 a = e: absorption coefficient = emissivity (Kirchhoff’s law)
Weak precipitation
signature over land

Strong precipitation
signature over ocean

Land
Ocean

32

Blackbody radiation (Planck function) (Wm -2 sr-1 m-1)

Wien’ law

Tropical Rainfall Measuring Mission (TRMM)••

Major characteristics

US-Japan joint
mission– Focused on rainfall observation. First
instantaneous rainfall observation by
three different sensors (PR, TMI, VIRS). PR,
active sensor, can observe 3D structure of
rainfall.
– Targeting tropical and subtropical region,and chose non-sun-synchronous orbit (inc.
angle 35 degree) to observe diurnal
variation.

Launch28 Nov. 1997 (JST)Major achievement in Japan

Altitude

About 350km (since 2001,
boosted to 402km to extend
mission operation）

Inc. angle

About 35 degree, non-sunsynchronous orbit

Design life

3-year and 2month (still
operating)Instruments

Precipitation Radar (PR)TRMM Microwave Imager (TMI)
Visible Infrared Scanner (VIRS)
Lightning Imaging Sensor (LIS)
CERES (not in operation)– More than 14 years rain observation dataarchive– Demonstration of high quality and highreliability of a satellite onboard
precipitation radar– Improvement of MWR precipitation
retrieval by PR 3D observation– Pioneering precipitation system
climatology by PR observation– Operational use in NWP etc.
– New products including all-weather SST,
global soil moistureJapan: PR,launchUS: satellite, TMI,
VIRS, CERES,
LIS, operation

TRMM Precipitation Radar

２m

２ｍ
DIV/COMB

Radar type
Pulse radar
Antenna type128-elem. WG slot
array
Beam scanning
Active phased array
Frequency
13.796, 13.802 GHz
Polarization
Horizontal
TX/RX pulse width 1.57 / 1.67μsec
RX band width
0.6 MHz
Pulse rep. freq.
2776 Hz
Data rate
93.5 kbps
Mass
460 kgDesigned Life time
3 years
Sensitivity< 0.5mm/h
Horizontal resolution 4.3 km (nadir)
Range resolution
250 m# of indpdt samples
64 (fading noise < 0.7
dB)
Swath width
215km
Observable range
Surface to 15km

36

37

3-D Observation of a Typhoon by TRMM
Aug. 2, 2000, 20:49-20:53 (Japanese local time)Rain intensity at H=2 km

Vertical cross section through the eye and
3D structure

PR realized observation of 3D structure of rain over ocean where few
observations had been available.
38

Global Monthly Accumulated Rain by TRMM/PR(Estimated Surface Rain 1997/12 – 2010/05)
350 km402 kmA-side

The total decrease in PR e_surface rain by altitude change is estimated to be
5.90% on average in a global scale.
2012-11-16
39
Datacontinuity was kept by calibration for B-side H/W.

Bside

GSMaP_NRT: A proto-type for GPM•••

Global rainfall map by merging TRMM, AMSR-E, andother satellite information.
0.1-degree lat/lon grid, hourly products.
GSMaP near-real-time version (GSMaP_NRT) is
distributed via internet– Available 4-hr after observation– Binary and text data has been freely available sinceOct. 2008 via password protected ftp site.
– Hourly browse images are also available.
– SSMIS (F16, F17) has been introduced into the NRT
system since June 2010.
– Introduction of AMSU/MHS (NOAA N15/16/18/19,
MetOp-A) into the NRT system is in preparation.

•

Reanalysis of GSMaP (GSMaP_MVK) in latest version
is underway.
– Processing of 2007 data is completed, and it has been
distributed to NRT registered users. .
– Use all available microwave imagers, including SSMIS,
AMSU, and MHS.

http://sharaku.eorc.jaxa.jp/GSMaP/

3-hourly animation of two Tropical
Cyclones over the South Pacific in40
March 2010 by GSMaP_NRT.

Global Distribution of the Mean Storm Height
Measured by the TRMM Precipitation Radar
July 1998

January 1999

図１０

41

図１１

42

(by Endo and Koike)43

7.5 km2 km

Latent heating from TRMM PR for 1998-2000.
(Takayabu et al.)44

EarthCARE / Cloud Profiling Radar
ESA Earth Explorer Core Mission

CPR

ATLID

MSI
BBR

Radar type

94 GHz Doppler Radar

Center frequency

94.05 GHz

Pulse width

3.3 micro second (equivalent to 500m
vertical resolution)

Beam width

0.095 deg

Polarization

Circular

Transmit power> 1.5 kW (Klystron spec.)

Height range-0.5 ~ 20 km

Resolution

500 m (100 m sample); Vertical,
500m integration; Horizontal

Sensitivity*-35 ~ +21 dBZ

Radiometric accuracy*< 2.7 dB

Doppler measurement

Pulse Pair Method

Cloud Profiling RADAR(CPR) Doppler range*
Doppler accuracy*
Joint Development
Pulse repetition frequency
of JAXA/NICT
Pointing accuracy-10 ~ +10 m/s< 1 m/s
Variable; 6100~7500 Hz< 0.015 degree*; at 10 km integration and 387 km orbit height
45

GPM

Concept of GPM

GPM = follow-on mission of the TRMM (Tropical Rainfall Measuring Mission)
Constellation Satellites

Core Observatory• Microwave Radio-meters
installed on each satelliteFrequent precipitation
measurement
precipitation• Dual-frequency Precipitation
Radar (DPR)• Microwave Imager (GMI)Highly sensitive
measurement
Calibration for constellation
radiometers
JAXA and NICT: DPR
NASA : Spacecraft bus and GMI
JAXA: H2A Launcher

Expected Partners:
NASA, NOAA, CNES-ISRO, China,
others2 satellites

NASA

3-hourly global
rainfall map

Blue: Inclination ~65º (GPM core)
Green: Inclination ~35º (TRMM)

8 satellites

46

Time and space Interval of the DPR database
1-day orbit

Color: orbit num

3-days orbit

Color: IFOV pixel num
0.50.5deg

100 pixels

One-day orbit observations ofDPR (upper) and GMI (lower)
The orbit data was calculated by Y.Iida (Osaka Prefecture Univ)

100 pixels

DPR dynamic database will be renewedin 15 deg × 5 deg grids every 3-10 47
days

Overview of GPM Core Observatory
Dual-frequency precipitation radar(DPR) consists of
Ku-band (13.6GHz) radar : KuPR
DPR
and
Ka-band (35.55GHz) radar : KaPR

GMI

Flightdirection

Altitude: 407km
Non-sunsynchronus circular orbit

Range resolution= 250m and 500m

KaPR
KuPR
Swath: 125
Swath: 245 km
km

GMI
Swath: 800km

5km
48Scanning Method
KuPR footprint
KaPR footprint (Matched with KuPR)
KaPR footprint (Interlaced)

49

26
1

Flightdirection

13 1: Dz=250m: Dz=250m: Dz=500m

25

37 25

49

KaPR: 125km (25+24beams)
KuPR: 245km (49beams)

The synchronized matchedbeam ( ) is necessary forthe dual-frequency
algorithm.

In the interlacing scan area ( ), the KaPR
can measure snow and light rain in ahigh-sensitivity mode with a double pulse
width.
49

Precipitation Measurement by DPR
Detectable range of KaPR (35.55 GHz)

Matched beam of
KuPR and KaPR

ICE

HeightDetectable range of KuPR (13.6GHz)

Sensitive
observation by the
KaPR

SNOW
Snowfallmeasurements in the
frigid zones

MELTING
LAYAR
RAIN

Discrimination ofsnow and rainusing differential
attenuation
methodKuPR
KaPR

Accurate rainfall estimation
using differential
attenuation method(DSD parameter estimation)
Accurate rainfall measurements in thetropics and the temperate zones

Radar reflectivity

50

(Maruyama and Fujiyoshi, JAS, 2005)
51

Radar measurement from space (1)‐‐ TRMM/PR and GPM/DPR rain retrieval algorithms‐‐
Toshio Iguchi
Applied Electromagnetic Research Institute,
National Institute of Information and Communications Technology
International Hydrological Programme
Precipitation Measurement from Space and its Applications
The Twenty‐second IHP Training Course
18 November ‐ 1 December, 2012
Nagoya, Japan

Remote sensing of rain by radar
RADAR: RAdio Detection And Ranging• Radar emits a known pulse ofradio waves and measures its
echoes from objects or targets.
• The time for the pulse to travel tothe target gives the distance tothe target.
• The direction of the radio waves
gives the direction of the target.
• The echo power depends on thesize and number of the targets.

Aircraft
Reflected wave

CloudsTransmitted wave
Rain
Radar

scattered wave

Current and future satellite missions
carrying a precipitation or cloud radar• Tropical Rainfall Measuring Mission (TRMM) / PR
– November 1997 – present– Single frequency (13.8 GHz),– 250 km swath, >18 dBZ, 250 m v. res.

• Global Precipitation Measurement (GPM) / DPR– 2014 (launch)– Dual‐frequency (13.6 GHz, 35.5GHz)– 250 km swath, >12dBZ (Ka, 125 km swath, 500 m vert. res)• CloudSat/CPR– April 2006 – present– 94GHz, nadir only, > ‐30dBZ, 500 m vertical res.

• EarthCARE/CPR– 2015(launch)– 94GHz, nadir only, > ‐35dBZ, 500 m vertical res.
– DopplerTropical Rainfall Measuring Mission: TRMM
Solar paddle

High-gain antenna

TMI

VIRS
LIS
PR

Observation of tropicalrainfall (Driving engine ofglobal atmosphere)
US‐Japan joint mission (Japan:
PR, Launch,
US: Bus, 4 sensors,operation)

CERES

Launched in Nov., 1997.
Still under operation
Orbit
Altitude
Inclination

Circular（Non-Sun Synchronous）
350km (402.5km since Aug. 2001) (±1.25km)
35 deg.

SensorPrecipitation Radar (PR)TRMM Microwave Imager (TMI)
Visible and Infrared Scanner (VIRS)First space‐borne
precipitation radardeveloped by CRL and
NASDA

Clouds and the Earth’s Radiation Energy System (CERES)

Lightning (LIS)COMMUNICATIONSRESEARCHLABORATORYTRMM Precipitation Radar

２m

DIV/COMB

Radar type
Pulse radar
Antenna type128-elem. WG slot array
Beam scanning
Active phased array
Frequency
13.796, 13.802 GHz
Polarization
Horizontal
TX/RX pulse width 1.57 / 1.67sec
RX band width
0.6 MHz
Pulse rep. freq.
2776 Hz
Data rate
93.5 kbps
Mass
460 kgDesigned Life time
3 years
Sensitivity< 0.5mm/h
Horizontal resolution 4.3 km (nadir)
Range resolution
250 m# of indpdt samples
64 (fading noise < 0.7 dB)
Swath width
215km
Observable range
Surface to 15km

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

PR: 降雨レーダ

PR:
Radar
TMI:Precipitation
TRMMマイクロ波観測装置
TMI: TRMM Microwave Imager
VIRS: 可視赤外観測装置
VIRS: Visible/IR Scanner

PR
VIRS

TMI

250 m
760 km215 km

720 km6～50km

4km
2km

3-D Observation of a Typhoon by the PR
Aug. 2, 2000, 20:49‐20:53 (Japanese local time)Rain intensity at H=2 km

Vertical cross section through the eye and
3D structure

PR realized observation of 3D structure of rain over ocean where few
observations had been available.

Monthly Rain Distributions estimated from the TRMM
PR data in 1998 (El Nino year) and 1999

Strom Top Height Distribution
measured with the TRMM Precipitation RadarPeculiarities of satellite‐borne radar
Differences from ground‐based radar• Hardware constraints– size, mass, power consumption• use of short waves ‐> attenuation• sensitivity– reliability• Observation geometry– distance, angle• sensitivity, resolution– surface behind rain
• surface clutter– moving platform (unless from a geostationary
satellite)• difficulty in Doppler measurement

Peculiarities of space‐borne radar• Spacecraft constraints– Power (500 W)– Antenna dimensions (2 m)– Orbit (350 km)• PR is a simple single‐frequency radar– No Doppler– No polarimetry• Observation geometry– large distance– nadir looking, surface echo (clutter)– high range resolution (250 m)– low horizontal resolution (4.5 km)– narrow swath (250 km)• Use of short wavelength (13.8 GHz)
– Attenuation correction• Sparse sampling in time at a given location• Various rain systems with different characteristics

Footprint size and wavelength• Use of relatively high frequency (short wave) to realize
a good horizontal resolution.
– antenna beam width ~ c1/D (wavelength/diameter)• : wavelength of the electromagnetic wave
• D: antenna diameter• c1: a constant that depends on the antenna illumination (~1.2)– footprint size ~ c1r/D (r: range to surface)– D < 2~3 m unless the antenna is developed on orbit– r > ~300 km.
– ‐> use a small  to make the footprint size (c1r/D) small.
– to realize a 5 km footprint with a 2 m antenna from a
400km orbit,  ~ 5*2/(1.2*400) m = 2.08 cm (= 14.4GHz)

Issues associated with short waves• Attenuation– rain, snow, water vapor, cloud liquid water (liquidcloud droplets), and oxygen molecules– Correction methods:• Hitschfeld‐Bordan method, Surface reference method• Non‐Rayleigh scattering effect– scattering cross section does not change
proportionally to D6– Drop size distribution modelPR Algorithm Flow and adjustable parameters
Received
Power P
Calibration

0

Attenuation
CorrectionmParticle
ProfileDSD
initial0m(R)PIA(HB)Ze

Ze‐to‐R
Conversion
Rain Rate• Calibration• Particle model– DSD parameters– particle profile• BB model• snow model0(REF)PIA(SRT)
Surface Reference
Technique (SRT)DSD
modifiedk‐Ze
relation••••0m(NR)
Databaserain

Rain TypeZm

no rainZe‐R
relation

Measurement errors
PIA errors
Rain profile in surface clutter
Inhomogeneity

Radar Equation

•

•

•
•
•
•

Factors that may affect the rain estimates fromspace‐borne radar
Principles of radar measurement of rain
– Conversion of received power (Pr) to apparent radar reflectivity factor (Zm)
(Calibration of instrument)– Conversion of Zm into effective radar reflectivity factor (Ze) (attenuation
correction)– Conversion from Ze to rain rate (R)Scattering and extinction characteristics of precipitation particles and their
vertical distribution (Type of precipitation: rain, snow, groupel, hail, etc.)– Drop size distribution (DSD)– Phase state, density (Mixing formula)– Shape and canting angle– Temperature (refractive index)
Fall velocity of precipitation particles (size, density, shape, vertical wind)Inhomogeneity of rain (Non‐uniform distribution of rain)
Scattering characteristics of sea and land surfaces
Attenuation due to constituents other than precipitation itself••

Clouds, water vapor, other gasses

Effect of multiple scattering (Ka band and above)

Drop Size Distribution (DSD)• Both k‐Ze and R‐Ze relations
depend on DSD.
• Hitschfeld‐Bordan’s solution
assumes a k‐Ze relation.
• When the SRT is not applicable,
the initial DSD determines theattenuation correction and theZe‐to‐R conversion.
• When the SRT is applicable, can be adjusted to match the H‐
B estimate of PIA to the SRT PIA.
This in effect corresponds toadjusting the initial DSD.

Hitschfeld-Bordan solution

DSD variation in Indian rain
Averaged DSDs in NE monsoon

Averaged DSDs in SW monsoon
4

2
1

-1

-1
2
3
Diameter (mm)4

5

R = 30 mm/h

1
0

1

R = 3 mm/h
R = 10 mm/h

2

0

0

R = 1 mm/h

3log10 of N(D)3
log10 of N(D)4

R = 1 mm/h
R = 3 mm/h
R = 10 mm/h
R = 30 mm/h

0

1

2

3

4

5

Diameter (mm)Averaged Dropsize Distribution during South-West (SW) and North-East (NE) monsoon seasons in Gadanki, south
India in 1997 and 1999. SW and NE seasons are between May and October, and between November andDecember, respectively.
DSDs within +/- 1 dB centered at the rain rate specified are averaged.
T. Kozu, K. K. Reddy and A.R. Jain
Oct. 20, 2000

South‐west
monsoonSeptember
1997
60(Sep.97)

60

50

50

40

40

30

dBZ

dBZ

Z‐R relations
in SW and NE Indian monsoon seasonsDec.'97 Z- R

30
20

20
10
0

10
dBR

20

y = 1.5602x + 19.623
R2 = 0.8539

10

y = 1.3385x + 25.952
R2 = 0.9165

0
-10

0
-10

30

Seasonal Variation of a and b in Z‐R
Transition
60
SW

a_97/10b_97*10a_99/10b_99*1080

a and bNorth‐east
monsoon(Dec.97)
December
1997

40

NE

20
0
5

6

7

8

9

Month

10

11

12

0

10
dBR

20

30

SW Z = 405R 1.29
NE Z = 144R 1.38
Strat/Conv separation:
Not significant

Surface Reference Technique

HeightDecrease of the apparent surface echo(  0 ) under rain is interpreted as the path‐
integrated attenuation (PIA) due to rain.
Attenuated
Echo profile
with Rain

Echo without
Rain

Echo after attenuation

correction

 ’0Rain Echo

Surfaceclutter 0

Surface Echo 0

Use this PIA to correct for
the attenuation of rain echonear the surface.
In practice, the difference
between the PIA to thesurface and the PIA to theclutter-free bottom must be
taken into account.

Rain and Surface Echoes

Vertical Cross Section of Radar Echo and Decrease of
Apparent Surface Cross Section

TRMM/PR Standard Algorithmr
Z m (r )=Z e (r ) exp 0.2 ln(10)  k ( s )ds  00k (r )   0 (r ) Z e (r ) 0

DSD assumed

Echo
Radar
Reflectivity
Factor Z

1 0

Z e ( rs )= Z m ( rs ) (1   )rsMeasured =0.2  0 ln(10)   0 ( s ) Z m ( s ) ds
0Actual0

PIA HB = 
PIA fin = 

10

0
10

0

log10 1   

Zm

HitschfeldBordan (HB)

log10 1    BayesianSurface Reference00
PIASRT= SRT－ m Technique (SRT)
0SRT is an estimate of 0e

rs

r

Ze
Path
Integrated
Attenuation
PIA

0m

0e

Surface Backscattering 23
Cross Section 0Other issues due to thenature of themeasurements

Shmizu et al. (2009)• main lobe and side lobe
clutter obscuring thenear surface echo, cancontaminate
meteorological echo
• Uncertain σ0 in complex
terrain• A priori DSD assumed
as a function of height.
Appropriate?
• Single frequency
measurements +unreliable PIA = limited
independent DSD
information

Rain‐type count and BB count‐ angle bin dependence ‐Thin: V6
Thick: V7 OAT(Courtesy of J. Awaka)

25

k profiles for Ze=40 dBZ
0 degree C height is assumed at 5km
The lapse rate is assumed to be-6 degrees/km.
The assumed profile has beenchanged to the red line in V7(ITE232).
(100% ice above the -20 degreelevel.)
It was the solid line before thechange(100% ice above the -15 degreelevel.)

Effect of non‐uniform rain distribution
Non-uniform, butvertically uniform

D0

dBZm
HeightdBZmHeightHeightdBZm

non-uniform

HeightHeightHeightUniform

D0

D0

Reasons for Revisions• Major reasons for the past revisions– Internal inconsistencies: angle‐bin dependence,– Disagreement of rain estimates between PR and TMI.
– Discrepancies between ground measurements and PR estimates
were found to be not significant enough to motivate therevision until the revision for version 7• ground measurements had large errors and comparisons between
satellite data and ground data on the same footings were not easy.

• Longevity of the TRMM satellite enables us to makeclimatological comparisons with ground data.
– Comparisons using histograms of rain rates in addition to thecomparisons with the total rain amount revealed
underestimation of rain with PR at many places over land invarious time intervals.

Agreement with TMI

Latitudesolid-line: V7
dashed-line: V6

red: PR
blue: TMI
V4

Precipitation Rate
(mm/month)Future Issues• ‐statistics shows a clear difference between rain over ocean
and rain over land.
– The current algorithm assumes common PSD parameters over ocean
and land for each storm type.
– A positive bias of  often found over ocean and a negative bias overland suggest that there are more small drops than the assumed PSD
over ocean and the opposite over land.
– Possibility of defining regionally dependent PSD models from theknowledge we accumulated in the past.

• Orographic rain.

– vertical structure of orographic rain may differ substantially from othertypes of rain.
– Estimating surface rain from the rain echoes at altitude much higher
than the surface involves a large error.
– Poor performance of SRT in mountainous regions amplifies the issue.

• Non‐uniformity of rain distribution within a footprint remains
to be a very complex but important issue to be solved in the
future.

Difference between radar andradiometer retrieval of rain
• Radar– well defined range information• Height information is very reliable.

– Z‐R relation scatters more than Tb‐R relation• Echo power is more sensitive to DSD variation than Tb.

– Number of wavelengths is very limited in practice.

• Radiometer– Needs to assume a vertical precipitation profile.
• e. g., freezing heightTime series of monthly mean rainfall anomalies
over Tropical ocean－Comparison among different passive microwave algorithmsー(W. Berg, et al.)Time series of monthly mean rainfall
anomalies over Tropical ocean(TRMM Ocean Retrievals)

TMI

PR

(W. Berg, et al.)

Passive Microwave Retrievals
Column integrated water vs rainfall rate

Freezing Level

Tb's in the low frequency channels of a microwave radiometerare proportional to the column integrated rain water content.

0C

0C

Rain Profile

Surface Rainfall

0C

Surface Rainfall

0C

Surface Rainfall

Surface Rainfall(Courtesy of W. Berg)PR Bright-Band Heightand TMI Freezing HeightTMI Freezing Height – PR Bright Band Height(W. Berg, et al.)PR and TMI Regional Validation

TMI V6, PR V5(W. Berg, et al.)GPM(Global Precipitation
Measurement Mission)

DPR(Dual‐frequency
Precipitation Radar)

(1)

(1)

GPM coreazimuthsatellite

KaPR:
35.5GHz

KuPR:
13.6GHz

Altitude
407km
(24)
(25)

(Scheduled for July 2013)

GPM Core Satellite
Core

Microwave
radiometer

Ku-band precip. radar

Ka-band precip. radar

Frequency
Sensitivity

Ku-band (13.6 GHz), Ka-band (35.5 GHz)
Ku-band --- same as TRMM PR (18 dBZ, z=250 m)
Ka-band --- same as Ku-band (18 dBZ, z=250 m)--- 6 dB better than Ku-band (12 dBZ, z=500 m)
Swath width 245 km (Ku), >100 km (Ka)
Footprint
5 km
Frequency
Swath width
Footprint

10 GHz --- 85 GHz (5 bands, 9 channels) --- similar to TMIApproximately 900 km (Conical scan)
40 x 60 km (10 GHz) --- 5 x 8 km (85 GHz)

Dual Frequency Precipitation Radar
14GHz
radar
beam

Detection limit in 35GHz channel

35GHz
radar
beam

Detection limit in 14GHz channel
Higher sensitivity at higher frequency

Ice
HeightSnow

Discrimination between snow and rain
by attenuation difference

Melting layer
Rain

14GHz
３５GHz

Accurate rain estimation basedon attenuation difference
Radar Reflectivity Factor

Measure 3‐D structure of rain as TRMM, but with better sensitivity
Accumulate climatological precipitation data continuously since TRMM
Improve estimation accuracy with dual‐frequency radar
Identification of hydrometer type
Estimation of DSD parameters

Main Characteristics of DPR
Item

GPM DPR

TRMM PR

KuPR

KaPR

Active Phased Array (128)Active Phased Array (128)Active Phased Array (128)Frequency

13.597 & 13.603 GHz

35.547 & 35.553 GHz

13.796 & 13.802 GHz

Swath Width

245 km120 km215 kmHorizontal Reso

5 km (at nadir)

5 km (at nadir)

4.3 km (at nadir)Tx Pulse Width

1.6 s (x2)

1.6/3.2 s (x2)

1.6 s (x2)Range Reso

250 m (1.67 s)

250 m/500 m (1.67/3.34 s)

250m

Antenna Type18 km to -5 km

18 km to -3 km

15km to -5km

(mirror image around nadir)(mirror image around nadir)(mirror image at nadir)

VPRF (4206 Hz170 Hz)

VPRF (4275 Hz100 Hz)

Fixed PRF (2776Hz)

Sampling Num

104～112108～11264

Tx Peak Power> 1013 W> 146 W

> 500 W

Min Detect Ze(Rainfall Rate)< 18 dBZ< 12 dBZ (500m res)< 18 dBZ( < 0.5 mm/hr )( < 0.2 mm/hr )( < 0.7 mm/hr )Measure Accuracy

within ±1 dB

within ±1 dB

within ±1 dB

Data Rate< 112 Kbps

< 78 Kbps< 93.5 Kbps

Mass

< 365 kg

< 300 kg

< 465 kgPower Consumption< 383 W

< 297 W

< 250 W

Observation Range

PRF

Size

2.4×2.4×0.6 m
1.44 ×1.07×0.7 m
2.2×2.2×0.6 m* Minimum detectable rainfall rate is defined by Ze=200 R1.6 (TRMM/PR: Ze=372.4 R1.54 )Beam Matching• Dimensions and shape– Same design for Ku and Ka antennas• Cross-track alignment– Granularity of 5-bit Phase shifter:– Res. = 2.4 m (<wxw

• Along-track alignment– Hardware alignment (not adjustable)– max. error: 0.02 deg=140 m– Shift the pulse timing, e.g. 1 PRT = 0.25 ms = 1.8 m• Scan direction– Hardware alignment (not adjustable)– max. error: 0.02 deg=17 m at scan edge  not sosignificant

New Factors in DPR Algorithm
Compared with TRMM PR, DPR will provide• More accurate estimates with higher sensitivity• Increased number of output variables– E.g. Two DSD parameters at each range bin.

by assuming• More detailed microphysical models
and using• More complicated algorithm– Combination of different algorithms• Optimum weights and combination among Zm(Ku),
Zm(Ka), SRT(Ka) and SRT(Ku) depend on region,
height, rain rate, etc.

Assumptions in the DF algorithms• DSD is parameterized by at most 2 free parameters.
– e.g., N0,  with known  in ‐distribution• Phase state of particles is known.
– Phase state is likely to be estimated from data.

• Temperature of rain drops is known.
– Uncertainty of temperature is a source of error but not large.

• There is a condition that makes the solution stable.
– SRT, N0 constant, etc.

• Rain is uniformly distributed in IFOV.
• Attenuation is only caused by precipitating particles. (Noattenuation by water vapor, cloud particles, etc. areconsidered.)– Cloud liquid water: Att(Ka) = 10 * Att(Ku), up to 5 dB
– Water vapor: Att(Ka) = 5 * Att(Ku), up to 1.5 dB near surface– Oxygen: Att(ka) = 5 * Att(Ku), 0.4 dB near surface

Basic Idea of Meneghini’s DF Algorithm• 2N(+2) observables (2N of Zm (and 2 of )) to estimate RR
at N range gates.
– If the relations among Z, R and k were constant, R would beoverdetermined.
– In fact, Z, R and k are functions of many parameters (DSD, phase, shape,
temp., vertical air velocity, non‐uniformity, etc.)• Parameterize DSD with two variables.
– E.g., N0 and D0、N0* and D0• Estimate these two parameters at each gate.
– 2N estimates from 2N(+2) observables• All other parameters are fixed.
– E.g. shape parameter in DSD, phase, temp, etc.

• Calculate R with the estimated parameters.
• Needs initial conditions (e.g., attenuations at a range)

DPR Rain Profiling Algorithm• At each range,r,Z e (r ; Ka ) / Z e (r ; Ku )  D0 (r )Z e ( r ; Ku ), D 0 ( r )  N 0 ( r )D 0 ( r ), N 0 ( r )  R ( r ), k ( r ; Ka ), k ( r ; Ku )• Range r to r+rk(r ; Ka), Ze (r ; Ka), Zm(r   r ; Ka)  Ze (r   r ; Ka)k (r ; Ku), Ze (r ; Ku), Zm (r   r ; Ku)  Ze (r   r ; Ku)• IterateDifference in Ze
D0 (mm)

T=20 CdBZe(13.6GHz) – dBZe(35.5GHz)(Meneghini et al., 2002)

Characteristics of DF algorithm(Ze‐ratio method)• can estimate two DSD parameters at
each range bin.
• generally works well under the given
assumptions (SRT available, no NUBF effect,etc.)– Random noise or quantization error in Pr does
not cause a serious bias error in retrieval.

Issues:– Multiple solutions possible for liquid
particles– Choice of DSD model (Closeness of modeldBZe34.5 - dBZe13.8
3

DSD to actual DSD)

• Actual variation of DSD is rather large (A. Tokay,
N. Adhikari)– separation of solid (ice) phase from liquidphase– inhomogeneity of rain within footprint– beam mismatching– attenuation caused by CLW and water vapor

2
dBZe34.5 - dBZe13.8•

10dBZe34.5 dBZe13.8

-1
-2
-3
-4
-5
1

2

3

4

log10(Ze13.8)

5

6

T.KozuBasic Structure of
DPR/L2 algorithm

Echo Power (L1)GANAL

Rain/no‐rain

Preparation
Module

0

Zm
T, P

Vertical Profile
Module

0*

SRT
DB

Type

Bright Band

k‐Z

phase

PIAsrtDSD Module

Solver Module

Cloud

Zm*

Classification
ModuleSRT Module

Vapor

Scattering
table

Precipitation Rate
PIAfinZe

DSD

Precipitation Water

Issues with DPR algorithm• Zm at 2 freq. are not necessarily always available atall range bins.
– sensitivity difference– large attenuation in Ka band
– limited applicable range in rain rate

• Effect of attenuation by clouds and water vapor– if the att. by clouds and WV are estimated accurately,
PIA(Ka)‐PIA(Ku) by precip. can be estimated well.

• A small bias in measurement may affect theestimates.
• Basic equations assume beam matching anduniformity of rain within IFOV.

Height

Applicable Range of DF Algorithm
region 0
region 1
region 2
region 3
region 4
region 5
region 6

Noise level in 35GHz channel
Noise level in 14GHz channel

DF algorithm applicablein regions 2, 3, and 4.

No signal
Snow, Ka only

Region 0: Nothing can be done.

Region 1: Use Z(Ka)-R relationship.
Snow, Ku and Ka
No attn. correction needed.
Region 2: Use DF algo. for snow.
Mixed, Ku and Ka
Attn. by WV, CW.
Region 3: Use DF algo for mixed rain
Rain, Ku and Ka
Needs int. value at r3b or r3t
Region 4: Use DF algo for rain.
Rain, Ku only
Needs int. value at r4b or r4t
Surface clutter Region 5: Use Ku SF algo for rain.
Needs init. value at r5b or r5t
Radar Reflectivity Factor
Region 6: Use a model profile
Ku Ze
Ku Zm (without noise)
Ku Zm (with noise)
Ka Ze
Ka Zm (without noise)
Ka Zm (with noise)

SRT gives attn. at r6b.
Region 5 appears only when
Ka attn. is large.

Airborne radar measurements over a weak convective cell and retrievals of thesize distributions in comparisons with the in-situ particle measurements: (a)T-39 radar measured reflectivity at nadir along the flight track shown in
Fig.2, (b) DFR of X and Ka bands at the altitude where the T-28 flew, as
indicated by the white line in Fig.3a, (c) comparisons of D0 between theradar estimated and the 2D-P measured results and (d) similar
comparisons for NT.
(Liao et al. 2003)

CloudSat CPR System Characteristics
•
•
•
•
•
•
•

Nominal Frequency 94 GHz
Pulse Width 3.3 µsec
Minimum Detectable Z* < ‐29 dBZ PRF 4300 Hz
Data Window 0‐25 km
Antenna Size 1.85 m
Dynamic Range 70 dB
Integration Time 0.16 sec
Nadir Angle (since 15 Aug 2006***) 0.16°
Vertical Resolution 500 m
Cross‐track Resolution 1.4 km
Along‐track Resolution** 1.7 km Data Rate 20 kbps•

*Equivalent radar reflectivity that gives a mean power equal to the standard deviation after
integration and noise subtraction. Atmospheric attenuation is not included.
**The along‐track resolution is based on averaging the instantaneous footprint over the
integration time. Based on purely geometric arguments, the along‐track resolution would beapproximately 2.5 km. However, a more rigorous convolution calculation gives an along‐track
resolution of 1.7 km, as shown in the table.
***Nadir angles were changed from approx. 1.71° to 0.0° on 7 July 2006 and from 0.0° to
0.16° on 15 August 2006••

Heritage: APR‐2 and ACR in Wakasa Bay (Japan), APR‐2 and CRS/EDOP in TC4 (East Pac).

Ku band

Altitude [km]Ka band

Altitude [km]W bandElapsed Time [min]Altitude [km]Altitude [km]Linear Depolarization Ratio

Altitude [km]dBZ

Altitude [km]ReflectivitydB

Ku band

Ka bandW bandElapsed Time [min](courtesy: S. Tanelli)What is EarthCARE ?
EarthCARE (Earth Clouds, Aerosols and Radiation Explore)• Satellite mission selected as the sixth Earth Explorer Mission
in 2004 of ESA• European Space Agency (ESA) and Japanese (JAXA, NICT)
collaboration• Objective: global clouds and aerosol vertical observation for
global radiation budget• Launch: 2015 (?)• Instrument:
Cloud Profiling Radar (CPR)
Atmospheric Lidar (ATLID)
Multi‐spectral Imager (MSI)
Broad Band Radiometer (BBR)
55

MSI (ESA)
Visible & infrared imager
150 km across track
Object: Clouds, Aerosol
Horizontal structure

Orbit• Sun‐synchronous• Inclination 97 degrees• Altitude: 450 km (TBD)• Local time: 13:30
descending node
• Period: 94 min• Mission life: 3 years

CPR (Japan)
W‐band Doppler radar
Nadir

BBR (ESA)

Object: Cloud, Rain

Broad band radiometer

Vertical structure+‐50 & 0 degree alongtrack
Object: Radiation flux (longand short wave)

ATLID (ESA)• Revisit period: 10 ‐ 30 days
(TBD)
Satellite• Mass: 1300 kg• Power: 1100 W•Data rate: up to 1500
kbit/sec

UV lidar4 degree forward(TBD)
Object: Cloud, Aerosol
Vertical structure

56

CPR structure and function
Rx2
EIK2
Tx2

Main reflector

QOF

Tx1

Deployment
mechanism

Rx1
EIK1

Quasi‐optical feeder

AntennaTransmitter
EIK

QOF

LNA
RecieverUp coverter

Platform for Tx/Rx

Pulse
modulaor

Standard
Oscilator

Down
converter
Data processor

57

CPR specifications
Tx frequency
Tx peak power

94.05 GHz
1.5 kW (EOL)

Pulse width

3.3μs

Polarization

Circular

Antenna diameter
Beam footprint size
Beam direction
Minimum sensitivity
Data sampling

2.5 m
700 m
Nadir
-35 dBZ (10km average)
100 m (Vertical)
500 m (Horizontal)
Pulse pair

Doppler measurement58Difference from CloudSat CPR•Higher sensitivity (about 10 dB) from bigger antenna andlower orbit
minimum detection: ‐35dBZ at TOA (10km integration)•Doppler function (pulse–pair method)
vertical velocity measurement
accuracy: 1 m/ s (10km integration ‐19dBZ echo)•Variable PRF and height range•Co‐registration with Lidar and other sensors•Circular polarization•Less ground clutter effect•Longer life transmit tube (EIK)59

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Microwave Radiometer Measurements
Methodologies to retrieve precipitation
Applications to data assimilation
Kazumasa Aonashi(MRI/JMA)
Nov. 23 2012

IHP training course@Nagoya Univ.

Motivation: Needs for Precip. Remotesensing• Precipitation monitoringis very important todisaster prevention.
• Precipitation observation
data are sparse,
especially over oceans.
IHP training course@Nagoya Univ.

Heavy Precipitation observed with TRMM
sensors(MCS near Southern Kyushu, Jun. 13, 1998)
IR Ch4 TBB(K)

Radar Precip.

MWR TB (19V)

MWR TB (85V)

IHP training course@Nagoya Univ.

Correlation of satellite-estimated and Radarestimated instantaneous rain rates vs normalized
RMS differences (Ebert & Manton, 1998)

IHP training course@Nagoya Univ.

Application: TC monitoring
Cyclone SIDR
(Nov. 12-15, 2007）

IHP training course@Nagoya Univ.

Time vs. Longitudinal Cross Section of
GSMaP_SSM/I Precipitation (5S-5N)

IHP training course@Nagoya Univ.

Outline• Introduction• Features of Microwave Radiometer Data
• Passive Microwave Precipitation Retrieval• Data Assimilation using Microwave
Radiometer Data

IHP training course@Nagoya Univ.

Global Rainfall Map Processing System at
JAXA/EORC
Near real time and high-resolution global rainfall map based onsatellite observation

Distribution

produced 4 hours
after observation andupdated every hour

Produced Rainfall Map

Distribution

http://sharaku.eorc.jaxa.jp/GSMaP/
IHP training course@Nagoya Univ.

Multi-Satellite Precip Composite（GSMaP_MWR, daily precip）
TMI+AMSR+AMSR-E+SSM/I (F13, F14, F15), 0.25ﾟ×0.25ﾟ

IHP training course@Nagoya Univ.

Outline• Introduction• Features of Microwave Radiometer Data
• Passive Microwave Precipitation Retrieval• Data Assimilation using Microwave
Radiometer Data

IHP training course@Nagoya Univ.

Microwave Radiometer Measurements
Features of Microwave Radiometer Data

Microwave properties of Precipitation
Satellite Microwave Radiometers (Imagers)
IHP training course@Nagoya Univ.

Microwave Range
Frequency ３－３００ＧＨｚ（Wavelength 1 mm~10cm)

IHP training course@Nagoya Univ.

Satellite Observation (TRMM)
Infrared Imager 10μm

Cloud Particles
Frozen Precip.
Snow Aggregates

Melting Layer
Rain Drops

Cloud Top Temp.
Scattering

3 mm-3cm

Microwave Imager
Radiation from Rain
Scattering by Frozen
Particles
Radar 2cm
Back scattering from Precip.

Electromagnetic waves
are most sensitive tohydrometers
0℃
Radiationwith their wavelengths

SST, Winds
IHP training course@Nagoya Univ.

Heavy Precipitation observed with TRMM
sensors(MCS near Southern Kyushu, Jun. 13, 1998)
IR Ch4 TBB(K)

Radar Precip.

MWR TB (19V)

MWR TB (85V)

IHP training course@Nagoya Univ.

Mie Scattering by each particle

σ sc = Qscπ r , Qsc is function of Re(nc )2

σ ab = Qabπ r , Qab is function of Im(nc )2where x = 2π r / λnc : complex index of refraction

IHP training course@Nagoya Univ.

Complex Index of Refraction

Emission&scattering
By liquidwater particles

Only Scattering
By frozenwater particles
IHP training course@Nagoya Univ.

Satellite Observation (TRMM)
Infrared Imager 10μm

Cloud Particles
Frozen Precip.

Cloud Top Temp.
Scattering

3 mm-3cm

Microwave Imager
Emit. & Scatt. by Rain
Scattering by Frozen
Particles
Radar 2cm
Back scattering from Precip.

Snow Aggregates

Melting Layer
Rain Drops

Emit. & Scat. by Rain
0℃
Scatteringby Frozen
Radiation
Particles

SST, Winds
IHP training course@Nagoya Univ.

Surface Emissivity•••

Surface Emissivity is >0.9 over dry land,
0.4~0.7 over sea
Surface Emissivity
over sea depends
surface wind speed,
SST.
Surface Emissivity
over land depends onsnow coverage, soil
moisture, vegitation,etc.

T − TB ≈ (1 − ε s )Te −2τ / μ ,when T ≈ Ts

IHP training course@Nagoya Univ.

Heavy Precipitation observed with TRMM
sensors(MCS near Southern Kyushu, Jun. 13, 1998)
IR Ch4 TBB(K)

Radar Precip.

MWR TB (19V)

MWR TB (85V)

IHP training course@Nagoya Univ.

Surface physical observable from MWR

Sea Ice

SST(including
cloudy area)

IHP training course@Nagoya Univ.

MWR TB sensitivity
Saturation (over ocean)• TB becomes close to Temp
as optical thickness increases(saturation)
T − TB ≈ (1 − ε s )Te −2τ / μ ,when T ≈ Ts

• After saturation TB decreases
mainly due to scattering.
• TB at Higher Frequencies are
more sensitive to rain intensity.
So saturation occurs at weakerintensity.
IHP training course@Nagoya Univ.

Physical Basis of Microwave Precip. Retrival• Over Land:
Scattering byfrozen particles(Higher Freq.)• Over Ocean:
Scattering(Higher Freq.) +Emission from Rain
(Lower Freq.)
IHP training course@Nagoya Univ.
N

23 2012

TRMM Microwave Imager (TMI)
Frequency：10.7 ＆ 19.4 ＆ 21.3 ＆ 37.0 ＆ 85.5 GHz
Resolution ：38.3 & 18.4 & 16.5 & 9.7 & 4.4 km
Swath：760 km
TRMM

Nov. 23 2012

IHP training course@Nagoya Univ.

IHP training course@Nagoya Univ.

MAJOR CHARACTERISTICS OF AMSR2
Freq (GHz)： 6.925 10.65 18.7 23.8 36.5 89.0
/7.3
IFOV (km)： 35x62 24x42 14x22 15x26 7x12 3x5
Swath width :1450 km

IHP training course@Nagoya Univ.

DMSP SSMIS• SSMIS: conically scanning MWR
with a 53.1 degree and a swath
width of 1707 km.
• Center Frequencies(GHz)19.35 22.235
37.0 91.665 150.0
183.31+/-1 183.31+/-3
183.31+/-7• Polarization
V/H V
V/H V/H H
H H H• IFOV (km x km)
73x47 73x47
41x31 14x13 14x13
14x13 14x13 14x13

IHP training course@Nagoya Univ.

SSMIS(2003~)

GPM Reference Concept
OBJECTIVE: Understandthe Horizontal andVertical Structure ofRainfall and Its
Microphysical Element.
Provide Training for
Constellation
Radiometers.

OBJECTIVE: Provide
Enough Sampling toReduce Uncertainty in
Short-term Rainfall
Accumulations.
Extend Scientific andSocietal Applications.

Core Satellite• Dual-frequency Precipitaion
Radar (JAXA and NiCT)• Multi-frequency Radiometer(NASA)• H2-A Launch (TBD)• TRMM-like Spacecraft• Non-Sun Synchronous Orbit• ~65° Inclination• ~407 km Altitude• ~5 km Horizontal Resolution• 250 m / 500m Vertical
Resolution

Precipitation Validation
Sites

Constellation Satellites• Small Satellites with
Microwave Radiometers• Aggregate Revisit Time,
3 Hour goal
• Sun-Synchronous Polar
Orbits• 500~900 km Altitude

Global Precipitation
Processing Center• Capable of Producing Global
Precipitation Data Products
as Defined by GPM Partners• Global Ground Based Rain
Measurement IHP training course@Nagoya Univ.

Microwave Radiometer Measurements
Passive Microwave Precipitation Retrieval

IHP training course@Nagoya Univ.

Outline• Introduction• Conventional Algorithm– Forward Calculation (precip cloud models)– Retrieval Part
– Validation (TRMM PR)• New over-land algorithm– MWI Indices for Over-land Algorithm– Validation (TRMM PR)• Summary
IHP training course@Nagoya Univ.

Global Rainfall Map Processing System at
JAXA/EORC
Near real time and high-resolution global rainfall map based onsatellite observation

Distribution

Produced Rainfall Map

produced 4 hours
after observation
andupdated everyhour
Distribution

http://sharaku.eorc.jaxa.jp/GSMaP/GSMaP Project

TRMM
TMI

AMSR-EAMSR2DMSP
SSM/I,SSMIS

NOAA
AMSU

MWR precip retrieval algotrithm
L2 Product from each sensor

Infrared radiomter
Cloud motion vector

Mix
Near Real Time
SystemAt JAXA/EORC

L3 Composite
product
0.1degree

Geo. Satellite

1 hour.
6 hour1 day
1 month

5th IPWG Workshop

Composite algorithm of
IR and MWR

0.1degree・30min

Conventional Algorithm

Forward Calculation
Retrieval Part
Validation (TRMM PR)

IHP training course@Nagoya Univ.

Physical Basis of Microwave Precip. Retrival• Over Land:
Scattering byfrozen particles(Higher Freq.)• Over Ocean:
Scattering(Higher Freq.) +Emission from Rain
(Lower Freq.)
IHP training course@Nagoya Univ.

Basic Idea of the Retrieval Algorithm

Forward calculation
Ts, Temp
Precip Profiles
DSD
Mixed phaseinhomogeneity

RTM

Observed
TBs

Look-up
Table
Precip.

Retrieval Calculation

Screening
Inhomogeneity
estimation
Scattering part
Emission partFind the optimal precipitation that gives RTM-calculated TBs
fitting best with the observed TBs:
PCT37, PCT85 (land)
TB10v,TB19v, TB37v, PCT37, PCT85 (sea)Fig.1

Parameters for RTM calculation
Precipitation Types

Atmospheric
Variables(T,Humidity)

Freezing
Level Height

Precipitation
ProfileFrozen Particles

Particle Size
Distribution

Mixed Phase Particle

Refractivityof Particles

Rain

Inhomogeneity

Surface Condition(Surface Temp, Sea-surface Wind)IHP training course@Nagoya Univ.

of Precipitation

Basic Idea of the Retrieval Algorithm

Forward calculation
Ts, Temp
Precip Profiles
DSD
Mixed phaseinhomogeneity

RTM

Observed
TBs

Look-up
Table
Precip.

Retrieval Calculation

Screening
Inhomogeneity
estimation
Scattering part
Emission partFind the optimal precipitation that gives RTM-calculated TBs
fitting best with the observed TBs:
PCT37, PCT85 (land)
TB10v,TB19v, TB37v, PCT37, PCT85 (sea)Basic Idea of the Retrieval Algorithm

Forward calculation
Ts, Temp
GANAL
Precip Profiles
Statistical
DSD
Precip-related
Mixed phase
Variable
inhomogeneity

Models (PR)RTM

Observed
TBs

Look-up
Table
Precip.

Retrieval Calculation

Screening
Inhomogeneity
estimation
Scattering part
Emission partFind the optimal precipitation that gives RTM-calculated TBs
fitting best with the observed TBs:
PCT37, PCT85 (land)
TB10v,TB19v, TB37v, PCT37, PCT85 (sea)Parameters used in the Algorithm:
Atmospheric & surface variables• Atmospheric variables(Temp,FLH), surface
variables(Ts, SSW, SST)
are derived from the Global
Analysis data of JMA

Temperature bias of
GANAL against sonde

Freezing
Level Heightfor Jan.1,
2003

IHP training course@Nagoya Univ.

Precipitation Profile Model
Precip type classification
Data base
10 types (land 6, sea 4) areclassified from TRMM PR data
(2.5 deg, 3 monthly)

Height from 1 deg level [km]Precip Profile(land) 0: thunderstorm, 1: shower, 2: shallow, 3:
frontal rain, 4: organized rain 5: highland(sea) 6: shallow 7:frontal rain,
8:transit, 9:organized rain

Precip profile data base
Example：TRMM PR averaged preciptation profiles
for each type, surface precip, conv/stra

1℃ level

Rainfall rate [mm/h]

IHP training course@Nagoya Univ.

Particle Size Distribution
DSD for rain: Kozu model (2A25 average distribution
calibrated with averaged epsillon)
epsillon =1 for stratiform rain

N ( D ) = N 0 D μ exp( − ΛD )PSD for frozen particles: Marshall-Palmer distribution

Data base of conv. Epsillon
Averaged for each precip type

IHP training course@Nagoya Univ.

Nishitsuji (Mixed-Phase) Modelfor Stratiform Rain
On the basis of the filed experiment, the following parameters are modeled

ε s −1
ε −1
ε −1
ε −1
Volume liquid water fraction （Pw)
= Pw w+ Pi i+ Pa a
εs +U
εw +U
εi +U
εa +U
shape parameter of the dielectric constant (U)DSD parameter (B) is a function of Pw
N ( D) = N 0 10 − Ba (m −3 mm −1 ) (a : radius in cm)
Density ρ=√Pw
Fall velocity Magono-Nakamura(1965) for snow and Foot and Du Toit for rain

Pw and U profile

Relationship between B and Pw

U

10
0

0

10

2

10

4

10

6

40

U
0.5

BB

Distance below BB (km)

•
•
•
•
•

PW
20

1

0

0.5
PW

1

–3

10

–2

10

IHP training course@Nagoya Univ.

PW

–1

10

0

10

LUT calculation (1)TBs for homogeneous precipdTB (τ , μ , ϕ )= TB −(1 − ω 0 )T (τ ) −
μ
dτ

ω0
4π



P(τ , μ , ϕ , μ ' , ϕ ' )TB (τ , μ ' , ϕ ' )dμ ' dϕ 'where μ = cosθ ,τ = K ab + K sc dz , ω 0 = K sc /( K ab + K sc ),P is phase function

Radiative Transfer Code (Liu,1998)• One-dimensional model (Plane-parallel)• Mie Scattering (Sphere)• 4 stream approximation• Calculate TBs for homogeneous, convective &stratiform precip with each precip types.
IHP training course@Nagoya Univ.

Basic Idea of the Retrieval Algorithm

Forward calculation

Precip-related
Variable Models
FLH
Precip Profiles
DSD
Mixed phaseinhomogeneity

Observed
TBs

Retrieval Calculation

RTM
Look-up
Table
Precip.

Screening
Inhomogeneity
estimation
Scattering part
Radiation partFind the optimal precipitation that gives RTM-calculated TBs
fitting best with the observed TBs:
PCT37, PCT85 (land)
TB10v,TB19v, TB37v, PCT37,
PCT85(sea)Univ.
IHP training
course@Nagoya

Flow of the retrieval part
Observed TBs
Detection of Precipitation Arearain flag
Inhomo. Estimation / LUT selection

LOOK-UP TABLE(LUT)

Inhomogeneity (PCT85)
Retrieval using Scattering Signals
Precip_scat ( PCT37, PCT85)

Retrieval using Emission Signals
Optimal Precip(TB10v,TB19v,Precip_scat, TB37v)

Over Ocean
Retrivals

IHP training course@Nagoya Univ.

Over Land
RetrievalsScreening Algorithm over land (Seto et al., 2005）
TB data base forRain free pixels

Obs
TB(85V)

Rainy

Retrieval

PrecipR≧0

Screening
STD &AverageRain if TBe(85V)－TB(85V)>0

No-RainTBe(85V)=a+b×TB(22V)－k0×σeHigh
Results

Accuracy isimproved comparedto GPROF

Right Answer

Comb 85GHz 22GHz

降雨判定アルゴリズム

PrecipR=0Rain free

IHP training course@Nagoya Univ.

low

cy
a
ur
c
Ac

Error Answer

Flow of the retrieval part
Observed TBs
Detection of Precipitation Arearain flag
Inhomo. Estimation / LUT selection

LOOK-UP TABLE(LUT)

Inhomogeneity (PCT85)
Retrieval using Scattering Signals
Precip_scat ( PCT37, PCT85)

Retrieval using Emission Signals
Optimal Precip(TB10v,TB19v,Precip_scat, TB37v)

Over Ocean
Retrivals

IHP training course@Nagoya Univ.

Over Land
Retrievals

LUT calculation (2):
LUTs for inhomo precip• The calculated TBs areconverted into TBs forinhomogeneous precip with
Aonashi and Liu’s method(2000).
• LUT used for retrieval isweighted average of convective& stratiform TBs.

2.0

1.5

SIGMA85• STD of Log(Pr) is estimatedfrom STD of Log(rain85)
statistically (Kubota et al, 2008).

LOOK-UP TABLE
(LUT)

1.0

0.414+.5

0.678*sigma85o
SIGMA85O
0.0
0.0

STDLGPRH
.5

1.0

1.5

STD LN(PR) (PCT85ra < 260K)

IHP training course@Nagoya Univ.

2.0

Flow of the retrieval part
Observed TBs
Detection of Precipitation Arearain flag
Inhomo. Estimation / LUT selection

LOOK-UP TABLE(LUT)

Inhomogeneity (PCT85)
Retrieval using Scattering Signals
Precip_scat ( PCT37, PCT85)

Retrieval using Emission Signals
Optimal Precip(TB10v,TB19v,Precip_scat, TB37v)

Over Ocean
Retrivals

IHP training course@Nagoya Univ.

Over Land
Retrievals

Cyclone Nargis (May, 2008)

IHP training course@Nagoya Univ.

PR and TMI precipitation (OP59618)

IHP training course@Nagoya Univ.

Rainspc.v4.10.20080417 vs PR rainsurf
over Sea for Jan,Apr,Jul,Oct 1998

IHP training course@Nagoya Univ.

Rainspc.v4.10.20080417 vs PR rainsurf
over LAND for Jan,Apr,Jul,Oct 1998

Nov. 12, 2008

The 18th IHP Training Course

Basic Idea of the Retrieval Algorithm

PCT37, PCT85 over land
Forward calculation
Ts, Temp
GANAL
Precip Profiles
Statistical
DSD
Precip-related
Mixed phase
Variable
inhomogeneity

Models (PR)RTM

Observed
TBs

Look-up
Table
Precip.

Retrieval Calculation

Screening
Inhomogeneity
estimation
Scattering part
Emission partFind the optimal precipitation that gives RTM-calculated TBs
fitting best with the observed TBs:
PCT37, PCT85 (land)
TB10v,TB19v, TB37v, PCT37, PCT85 (sea)MWI indices for Over-Land algorithm

PCT37, PCT85 over land
Forward calculation
Ts, Temp
GANAL
Precip Profiles
Statistical
DSD
Precip-related
Mixed phase
Variable
inhomogeneity

Models (PR)RTM

Observed
TBs

Look-up
Table
Precip.

Retrieval Calculation

Screening
Inhomogeneity
estimation
Scattering part
Emission part

A priori information error is assumed to be the main cause of
the retrieval bias.
Introduction of MWI indices which the retrieval bias depends on.
Correction LUT based on the above dependency.

Summary of forward calculation
experiments• Sensitivities of TB depressions to precip variables
PSD offrozenparticles

85

◎

◎

◎

○

×

37

○

○

×

◎

○

(GHz)

Nonspherical
Particles

Freezing
DSD oflevel height rain
(FLH)

Depth offrozenprecipFreq• TB85 depression was very sensitive to frozen precip
properties (Dtop, PSD, shapes)• TB37 depression was sensitive to FLH and Rain DSD
in addition to frozen precip properties.
IHP training course@Nagoya Univ.

Index of Dtop, R8537• TMI R8537 increases
with Dtop estimated fromPR.

Retrain.v4.10.20080417
match-up data (Land ‘98)
50
40
30
20
10
5
4
3
2
1

R8537.r37>0.3• R8537 expressed asratio of precipitation
retrieved from TB85
(Rain85) to TB37
(Rain37) using theconventional GSMaP
algorithm.

.5
.4
.3
.2
.1

R 2 乗 = 0.2419
0

2000

4000

toplev-flh

IHP training course@Nagoya Univ.

6000

8000

10000

Index of FLH: PCT37 with no rain
For each rainy pixel, PCT37 with no rain (PCT37nr)
is derived from surrounding no rain pixels.
GANAL tends to over (under)estimate PCT37nr
over cold (hot) regions.

CPCT370m

CPCT370m

PCT37nr

IHP training course@Nagoya Univ.

PCT37nrNew Over-Land Algorithm:
Statistical correction of LUTs
using (PCT37nr,R8537)• TRMM data sets for 1998 are classified by(R8537,PCT37nr) .
• Linear fitting coefficients between Rain37,
Rain85 and PR surface precipitation rates.

IHP training course@Nagoya Univ.

Rain85 vs PR rainsurf depending on R8537(1998, over Land )R8537
1.4-2.1

R8537
< 0.7

PR Rainsurf (mm/hr)

Rain85 (mm/hr)

PR Rainsurf (mm/hr)

Rain85 (mm/hr)

R8537
0.7-1.4

Rain85 (mm/hr)

Rain85 (mm/hr)

R8537
> 2.1

PR Rainsurf (mm/hr)

PR Rainsurf (mm/hr)

Retrieval bias of Rain85 is mainly due to Dtop error

Rain37 vs PR rainsurf depending on PCT37nr(1998, over Land )PCT37nr
290-300 K

PR Rainsurf (mm/hr)

PCT37nr< 280 K

PR Rainsurf (mm/hr)

Rain37 (mm/hr)

Rain37 (mm/hr)

PCT37nr
280-290 K

Rain37 (mm/hr)

Rain37 (mm/hr)

PCT37nr> 300 K

PR Rainsurf (mm/hr)

PR Rainsurf (mm/hr)
IHP training course@Nagoya Univ.

Rain37 vs PR rainsurf depending on dPCT37nr
PCT37nr ~(280-290 K) (1998, over Land )dPCT37nr>5K

Rain37 (mm/hr)

Rain37 (mm/hr)

dPCT37nr
0-5 K

PR Rainsurf (mm/hr)

PR Rainsurf (mm/hr)

dPCT37nr< -5 K
Rain37 (mm/hr)

Rain37 (mm/hr)

dPCT37nr-5 – 0 K

PR Rainsurf (mm/hr)

PR Rainsurf (mm/hr)

dPCT37nr=(PCT37nr-CPCT370m)
IHP training course@Nagoya Univ.

Rain37 vs PR rainsurf depending on PCT37nr
dPCT37nr= (-3 ~ +3 K)(1998, over Land )PCT37nr
290-300 K

PR Rainsurf (mm/hr)

PR Rainsurf (mm/hr)

PCT37nr< 280 K

Rain37 (mm/hr)

Rain37 (mm/hr)

PCT37nr
280-290 K

dPCT37nr=(PCT37nrCPCT370m)

Rain37 (mm/hr)

Rain37 (mm/hr)

PCT37nr> 300 K

PR Rainsurf (mm/hr)

PR Rainsurf (mm/hr)

Forward calculation error is main cause of Rain37 biases .

Validation Results

Over-land retrieval for 2004

IHP training course@Nagoya Univ.

Comparison of over-land retrievals
Rainsurf vs.Rainspc over Land (Jul. ‘04 )New Algorithm

Rainspc(mm/hr)Rainspc(mm/hr)Conventional Algorithm

PR Rainsurf (mm/hr)

Rainspc(mm/hr)Rainspc(mm/hr)PR Rainsurf (mm/hr)

LAT

IHP training course@Nagoya Univ.

LAT

Comparison of over-land retrievals
Rainsurf vs.Rainspc over Land (Jul. ‘04 )Conventional Algorithm

New Algorithm

Rainspc(mm/hr)Rainspc(mm/hr)

PCT37nr> 300 K

PCT37nr
290-300 K

PR Rainsurf (mm/hr)

Rainspc(mm/hr)Rainspc(mm/hr)PR Rainsurf (mm/hr)

PR Rainsurf (mm/hr)

PR Rainsurf (mm/hr)
IHP training course@Nagoya Univ.

Comparison of over-land retrievals
Rainsurf vs.Rainspc over Land (Jul. ‘04 )Conventional Algorithm

Rainspc(mm/hr)Rainspc(mm/hr)

PCT37nr
280-290 K

New Algorithm

PCT37nr< 280 K

PR Rainsurf (mm/hr)

Rainspc(mm/hr)Rainspc(mm/hr)PR Rainsurf (mm/hr)

PR Rainsurf (mm/hr)

PR Rainsurf (mm/hr)
IHP training course@Nagoya Univ.

Daily precip (mm/day) of rain37 andtheir difference from PR rainsurf:
over land for Jul. ‘03
Conventional Algorithm

New Algorithm

IHP training course@Nagoya Univ.

Summary• Conventional Algorithm– Forward Calculation (precip cloud models)– Retrieval Part
– Validation (TRMM PR)• New over-land algorithm– MWI Indices for Over-land Algorithm– Validation (TRMM PR)• Future directions

IHP training course@Nagoya Univ.

Orographic Rainfall(Shige & Taniguchi, 2010)
Freezing Level
Orographic

Warm rain withless ice
precipitation

A largeamount ofcondensates

Ice

Low-level
orographic
lifting

Water

Maritime Air
Ocean

6th IPWG Workshop

MountainMicrowave Radiometer
Measurements
Data Assimilation using
Microwave Radiometer Data

IHP training course@Nagoya Univ.

Outline
What is “Data Assimilation”?
Ensemble-Based Variational
Assimilation Method (EnVA)
EnVA to Incorporate Microwave
Imager Data into a Cloud-Resolving
Model
Summary & Discussion

IHP training course@Nagoya Univ.

Definition of Data Assimilation

Need for Statistical Approach
Historically, DA started with interpolation of theobserved data.
Interpolation is not appropriate in practice.

Use of first guess of previous NWP forecast or
NWP constraints.
Need for statistical approach to produce analysis
data:

Represent mathematically the error of the observation,
the NWP first guess, and constraints.
Find optimal values that minimize the analysis error.

IHP training course@Nagoya Univ.

Modeling of the errors
Observation error εo=Y-H(Xt)
where observation (Y) and the model counterpart H(X)First guess & analysis error εf=Xf – Xt, εa=Xa-Xt
where Xf,Xa are First guess & analysis of the model stateP = (ε − ε ) (ε − ε )
f

f

f

t

f

f

Bayes’ rule (conditional PDF)
Conditional PDF of X given obs. Y:

Prof ( X | Y ) ∝ Prof (Y | X ) Prof ( X )If errors are Gaussian:oProf (Y | X ) = Prof (ε )∝ exp{−0.5(Y − H ( X ))t R−1 (Y − H ( X ))}Prof ( X ) = Prof (ε f )∝ exp{−0.5( X − X f )t P − f ( X − X f )}The maximum likelyhood state is the one
minimizes the cost function J:
J : ( X − X f )t P − f ( X − X f ) + (Y − H ( X ))t R −1 (Y − H ( X ))IHP training course@Nagoya Univ.

Simple Example
We have a temperature observation To withSTD σo, given first guess Tf with STD σf.
The maximum likelyhood state is the one
minimizes the cost function J:
J:

1

1

σ

σ

f 2
(
) +
T
−
T
f2

∂J / ∂T =

2

σ

o 2
(
)
T
−
T
o2

(T − T ) +f2(T − T )oσ(T o / σ o 2 ) + (T f / σ f 2 )a∂J / ∂T = 0 → T =(1/ σ o 2 ) + (1/ σ f 2 )f2o2

IHP training course@Nagoya Univ.

Ta
Tf

To

Ensemble-Based Variational Assimilation Method

Basic idea of EnVAIHP training course@Nagoya Univ.

Goal: Data assimilation of MWI TBs into CRMs
Cloud Reslv. Model+ Data Assim System

Hydrological Model

MWI TBs(PR)
Precip.

IHP training course@Nagoya Univ.

Basic Idea of Ensemble-based assimilation
Analysis ensemble mean

Obs. Estimating first-guess error
with Ensemble forecastsR

Pef ≈Analysis w/ errors
T=t0

δ X tf1 (δ Xtf1 )T
N −1

FCST ensemble mean
T=t1
IHP training course@Nagoya Univ.

T=t2

Why Ensemble-based method?:
Heavy Rain Area

200km

Rain-free Area

10km

Ensemble forecast error corr. of PT (04/6/9/22 UTC)IHP training course@Nagoya Univ.

Why Ensemble-based method?:
To estimate the flow-dependency
Heavy
AreacovarianceRain-free Areaof theRain
error
200km

10km

Ensemble forecast error corr. of PT (04/6/9/22 UTC)IHP training course@Nagoya Univ.

EnVA: min. cost function in theEnsemble forecast error subspace

Minimize the cost function with non-linear Obs. term.





−1
−1
J x = 1 2 ( X − X f ) Pf ( X − X f ) + 1 2 (Y − H ( X )) R (Y − H ( X ))Assume the analysis error belongs to the Ensemble
forecast
error subspace （Lorenc,
2003):




f
f/2
Ω=[w1 , w 2 ,,,,, w N ]X − X ＝Ｐe  Ω
Pe

f /2

f
f
f
f
f
= [ X 1 − X , X 2 − X ,,,, X N − X f ]Forecast error covariance is determined by localization

P =P  Sffe

Cost function in the Ensemble forecast error
subspace：J (Ω) = 1 2 trace{Ωt S −1Ω} + 1 2{H ( X (Ω)) − Y }t R −1{H ( X (Ω)) − Y }IHP training course@Nagoya Univ.

Why Variational Method ?

To address the nonlinearity of TBs
MWI TBs are non-linear function ofvarious CRM variables.
TB becomes saturated as optical
thickness increases:
T − TB ≈ (1 − ε s )Te − 2τ / μ ,when T ≈ Ts

TB depression mainly due tofrozen precipitation becomesdominant after saturation.
IHP training course@Nagoya Univ.

Detection of the optimum analysis
Detection of the optimum Ω a , wa by minimizing J
where Ω is diagonalized with U eigenvectors of S：
χ i (m) = 1 d m {U t Ω}i (m)Approximate the gradient of the observation withthe finite differences about the forecast error:∂H ( X ) / ∂Ω ~ {H ( X + αδ pi ) − H ( X )}/ αfTo solve non-linear min. problem, we performed
iterations.
Following Zupanski (2005), we calculated
a theanalysis of each Ensemble members, Xi from theEnsemble analysis error covariance.

IHP training course@Nagoya Univ.

EnVA to incorporate MWR TBs into a CRM

Problems in EnVA for CRM
Displacement error correction
Application results

IHP training course@Nagoya Univ.

Problem in EnVA (1): Displacement error
AMSRE TB18v (2003/1/27/04z)

Large scale
displacement errors ofrainy areas between the
MWI observation andEnsemble forecasts
Presupposition of
Ensemble assimilation
is not satisfied inobserved rain areas
without forecasted rain.

Mean of Ensemble Forecast(2003/1/26/21 UTC FT=7h）

IHP training course@Nagoya Univ.

Presupposition of Ensemble-based assimilation
Analysis ensemble mean

Obs.
R

Ensemble forecasts haveenough spread to include(Obs. – Ens. Mean)Pef ≈Analysis w/ errors
T=t0

δ X tf1 (δ Xtf1 )T
N −1

FCST ensemble mean
T=t1
IHP training course@Nagoya Univ.

T=t2

Ensemble-based assimilation for observed rain
areas without forecasted rain
Analysis ensemble mean Obs.
R

Assimilation can giveerroneous analysis
when thepresupposition is notsatisfied.

Signals from rain can bemisinterpreted as thosefrom other variables
Analysis w/ errors
T=t0

FCST ensemble mean
T=t1

T=t2

Displacement error correction is needed!
IHP training course@Nagoya Univ.

Displacement error correction(DEC)+EnVA
Methodology
Application results for Typhoon CONSON(T0404)Case
Assimilation Results
Impact on precipitation forecasts

IHP training course@Nagoya Univ.

Displaced Ensemble variational
assimilation methodIn addition to X, we introduced d to assimilation.

The optimal analysis value maximizes ： farg max P( X , d | Y , X )


 
 
f
f
f
P( X , d | Y , X ) = P(d | Y , X ) P( X | d , Y , X )Assimilation results in the following 2 steps:a1) DEC scheme to derive d from P(d | Y , X f )2)EnVA scheme
using the DEC Ensembles toa aderive X from P( X | d , Y , X f )IHP training course@Nagoya Univ.

Problem in EnVA (1): Displacement error
AMSRE TB18v (2003/1/27/04z)

Large scale
displacement errors ofrainy areas between the
MWI observation andEnsemble forecasts
Presupposition of
Ensemble assimilation
is not satisfied inobserved rain areas
without forecasted rain.

Mean of Ensemble Forecast(2003/1/26/21 UTC FT=7h）

IHP training course@Nagoya Univ.

Assimilation method
Fig. 1:
CRM Ensemble
Forecasts

MWI TBs

Y( X f , Pe f ,TB c ( X f ))( X f ( d ), Pe f ( d ),TB c ( X f ( d )),TB c ( X i f ( d )))Displacement Error
Correctiond
Ensemble-based
Variational
Assimilation

IHP training course@Nagoya Univ.

( X a , Pea ,aXi )DEC scheme: min. cost function for d
Bayes’ Theorem



f
f
f
P(d | Y , X) = P(Y , X | d ) P(d ) / P(Y , X )P (Y , X f | d) can be expressed as the cond. Prob. of Y
given X f (d )：

P (Y , X f | d ) = exp{−1 2 (Y − H ( X f (d ))t R −1 (Y − H ( X f (d ))}2
We assume Gaussian dist. of P(d ) ： P(d ) = exp{−( d / 2σ d2 )}whereσ d is the empirically determined scale of thedisplacement error.
We derived the large-scale pattern of d by minimizing
J d (Hoffman and Grassotti ,1996) ： t −12
1ff
J d = (Y − H ( X (d ))) R (Y − H ( X (d )))} + d 2 σ d2
2
IHP training course@Nagoya Univ.

Detection of the large-scalepattern of optimum displacement
We derived the large-scale pattern of d from J d ,following Hoffman and Grassotti (1996) ： t −12
1ff
J d = (Y − H ( X (d ))) R (Y − H ( X (d )))} + d 2 σ d22r into the control variable in wave
We transformed
space, d using the double Fourier expansion.

We used the quasi-Newton scheme (Press et al.
1996) to minimize the cost function in wave space.

we transformed
the optimum r into the large-scalepattern of d by the double Fourier inversion.
IHP training course@Nagoya Univ.

Assimilation method
Fig. 1:
CRM Ensemble
Forecasts

MWI TBs

Y( X f , Pe f ,TB c ( X f ))( X f ( d ), Pe f ( d ),TB c ( X f ( d )),TB c ( X i f ( d )))Displacement Error
Correctiond
Ensemble-based
Variational
Assimilation

IHP training course@Nagoya Univ.

( X a , Pea ,aXi )Case （2004/6/9/22 UTC) TY CONSONTMI TB19ｖ

RAM (mm/hr)

IHP training course@Nagoya Univ.

Case （2004/6/9/22 UTC) TY CONSON

Assimilate TMI TBs
TMI 21v
TB19ｖ(10v, 19v,
) at 22UTC

RAM (mm/hr)

IHP training course@Nagoya Univ.

Ensemble Forecasts & RTM code
Ensemble forecasts
100 members started with
perturbed initial data
at 04/6/9/15 UTC (FG)Geostrophically-balanced
perturbation (Mitchell et al. 2002)
plus Humidity

Ensemble mean (FG)
Rain mix. ratio

TB19v cal. from FG
RTM: Guosheng Liu (2004)
One-dimensional model (Plane-parallel)Mie Scattering (Sphere)4 stream approximation
IHP training course@Nagoya Univ.

TB19v from TMI and CRM outputs
FG：

DE：

First
guess

After
DEC

TMI
CN：
DE+
EnVA

ND:
NoDE+
EnVA

IHP training course@Nagoya Univ.

RAM and Rain mix. ratio analysis (z=930m)
FG

DE
RAM

ND

CN

IHP training course@Nagoya Univ.

RH(contours) and W(shades) along N-S

FG

DE
Ｎ
M

Ｓ
ND

CN
Ｓ

Ｎ

Ｓ
IHP training course@Nagoya Univ.

ＮHourly Precip. forecasts (FT=0-1 h) 22-23Z 9th
FG
DE

RAM
ND

CN

IHP training course@Nagoya Univ.

Hourly Precip. Forecasts (FT=3-4 h) 01-02Z 10th
FG
DE

RAM
ND

CN

IHP training course@Nagoya Univ.

Summary
Ensemble-based data assimilation can giveerroneous analysis, particularly for observedrain areas without forecasted rain.
In order to solve this problem, we developed
the Ensemble-based assimilation method thatuses Ensemble forecast error covariance withdisplacement error correction.
This method consisted of a displacement error
correction scheme and an Ensemble-based
variational assimilation scheme.

IHP training course@Nagoya Univ.

Summary
We applied this method to assimilate TMI TBs(10, 19, and 21 GHz with vertical polarization)
for a Typhoon case (9th June 2004).
The results showed that the assimilation of
TMI TBs alleviated the large-scale
displacement errors and improved precip
forecasts.
The DEC scheme also avoided
misinterpretation of TB increments due toprecip displacements as those from othervariables.

IHP training course@Nagoya Univ.

Problem in EnVA (2): Sampling error
Forecast error corr. of W (04/6/9/15z 7h fcst)
200 kmHeavy rain
(170,195)

Weak rain(260,210)

Rain-free(220,150)

IHP training course@Nagoya Univ.

200 kmProblem in EnVA (2): Sampling error
Forecast error corr. of W (04/6/9/15z 7h fcst)
200 kmHeavy rain
(170,195)

Severe sampling
Weak rain(260,210)error for preciprelated variables

Rain-free(220,150)

IHP training course@Nagoya Univ.

200 kmThank you for your attention.

IHP training course@Nagoya Univ.

Combined use of radar and radiometer for
precipitation retrieval
Hirohiko Masunaga
Hydrospheric Atmospheric Research Center, Nagoya University

Combined sensor retrieval: Outline
 IntroductionMicrowave remote sensing A useful window of atmosphere

Radar and microwave radiometer Why not using them together?

 Algorithm description

How it works and how it is helpful.

 Toward future applications


2

From TRMM to GPM

22th IHP Training Course

Nov 22, 2012

Introduction

22th IHP Training Course

Nov 22, 2012

Different bands for different targets
 Visible lightAtmospheric particle scattering Cloud and aerosol optical properties

 Infrared radiation

Cloud top temperature Molecular absorption Temperature and humidity sounding Other gaseous compositions (e.g., CO 2)

 Microwave
Sensitive to liquid water when particles are large
 Precipitation retrieval

Oxygen and water vapor absorption
 Temperature and humidity sounding
22th IHP Training Course

Nov 22, 2012

Atmospheric Transmittance
InfraredUltra- Visible
VioletH2O
O３

CO2

Microwave

O３

H2O

O２
H2O

1/1000ｍｍ

H2O

CO2

Wavelength

1/10ｍｍhttp://earthobservatory.nasa.gov

22th IHP Training Course

Nov 22, 2012

Microwave spectrum of atmosphere

Water
Vapor

22.2 GHz

183 GHz60 GHz

Oxygen119 GHz

22th IHP Training Course

Nov 22, 2012

Passive versus Active sensors
 Passive remote sensors
Measure natural-origin radiation
 Imager, radiometers, and soundersObserve two-dimensional plan view.
 Except for sounders, which measure vertical profiles as well.



Relatively inexpensive

 Active remote sensors
Send electromagnetic pulses and detect the strength andtemporal delay of back-scattered signal.
 Radars and lidars
 c.f., scatterometers

Observe three dimensional structure Tend to be expensive

22th IHP Training Course

Nov 22, 2012

Radar and radiometer observations
Typhoon Cimaron (2006) observed by the Tropical Rainfall Measuring Mission (TRMM) satellite
http://trmm.gsfc.nasa.gov/publications_dir/extreme_events.html

Infrared image in white

3D rainstructureby radar

Surface rain bymicrowave
radiometer

22th IHP Training Course

Nov 22, 2012

Radar measurement principles
 Radar : Radio Detecting and Ranging

Observes back-scattered echo from rain drops.
 Sensitive to large rain drops but insensitive to small ones.
 Depends strongly on the drop size distribution (DSD).
 Rain rate could vary with your choice of Z-R relation.
rCPr = 2 Z exp − 2  k a ρdr 0r

Pr: Received powerr

Attenuation

Reflectivity factor

Z =  D 6 N ( D)dD
N(D): Rain drop size distribution (DSD)
22th IHP Training Course

Nov 22, 2012

Radiometer measurement principles
 Microwave radiometer: a scanning “radio telescope”

dTb= −Tb + (1 − ω )Tw + ω  P (Ω, Ω' )Tb ' dΩ'dτ extinction thermal
particle scattering
emissionω~0 (w/o scattering)

Clouds and rainfall emit thermal radiationbrighter than the background surface emission.
Typical of low frequency channels (10,19GHz etc)ω~1 (scattering only)

Scattering by precipitation particles shields fromthe background surface emission.
Typical of high frequency channels (89GHz etc.)22th IHP Training Course

Nov 22, 2012

Microwave imagery: 6GHz
http://sharaku.eorc.jaxa.jp/AMSR/index_j.htm

22th IHP Training Course

Nov 22, 2012

Microwave imagery: 36GHz
http://sharaku.eorc.jaxa.jp/AMSR/index_j.htm

22th IHP Training Course

Nov 22, 2012

Microwave imagery: 89GHz
http://sharaku.eorc.jaxa.jp/AMSR/index_j.htm

22th IHP Training Course

Nov 22, 2012

Microwave imagery: Retrieved rainfall
http://sharaku.eorc.jaxa.jp/AMSR/index_j.htm

22th IHP Training Course

Nov 22, 2012

Satellites with radar and radiometer onboard
 Tropical Rainfall Measuring Mission (TRMM)

Precipitation Radar (PR) Ku-band (13.6 GHz) radar TRMM Microwave Imager (TMI) 9 channels from 10 GHz to 85 GHz
 Visible/Infrared Scanner (VIRS) etc.
 In operation since late 1997


 Global Precipitation Measurement Mission (GPM)

Dual-frequency Precipitation Radar (DPR) Ku- (13.6 GHz) and Ka-band (35.5 GHz) radar GPM Microwave Imager (GMI) 13 channels from 10 GHz to 183 Ghz
 To be launched in 2014.


22th IHP Training Course

Nov 22, 2012

TRMM satellite scanning geometry
Visible/Infrared
Scanner (VIRS)
Precip Radar(PR)

TRM M Microwave
Imager (TMI)

http://www.eorc.jaxa.jp/TRMM/
channel/index_j.htm

観測視野（FOV)
Cross-track scan
(VIRS and PR)

Conical scan
(TMI)
22th IHP Training Course

Nov 22, 2012

Algorithm description

22th IHP Training Course

Nov 22, 2012

Existing combined sensor algorithms
 TRMM standard combined algorithm (2B31)


Haddad, Z. S., E. A. Smith, C. D. Kummerow, T. Iguchi, M. R. Farrar, S. L. Durden,
M. Alves, and W. S. Olson, 1997: The TRMM ‘day-1’ radar/radiometer combined
rain-profiling algorithm. J. Meteor. Soc. Japan, 75, 799–809.

 GPM standard combined algorithmGrecu, M., W. S. Olson, and E. M. Anagnostou, 2004: Retrieval of precipitation
profiles from multiresolution, multifrequency active and passive microwave
observations. J. Appl. Meteor., 43, 562–575.

Grecu, M., L. Tian, W. S. Olson, and S. Tanelli, 2011: A robust dual-frequency radar
profiling algorithm. J. Appl. Meteor.Clim., 50, 1543–1557.

 Goddard Profiling (GPROF) algorithm module

Masunaga, H., and C. D. Kummerow, 2005: Combined radar and radiometer
analysis of precipitation profiles for a parametric retrieval algorithm. J. Atmos.
Oceanic Technol., 22, 909–929.

Munchak, S. J., and C. D. Kummerow, 2011: A Modular Optimal Estimation
Method for Combined Radar-Radiometer Precipitation Profiling, J. Appl.
Meteor.Clim., 50, 433-448.
22th IHP Training Course

Nov 22, 2012

Flowchart

22th IHP Training Course

Nov 22, 2012

Flowchart

22th IHP Training Course

Nov 22, 2012

Initial CRM database Cloud-Resolving Model (CRM) database

Goddard Cumulus Ensemble Model (GCE) and UWNonhydrostatic Modeling System.
 About 30 snapshots from different simulations Tropical convection & squall line Hurricane
 Mid Atlantic cold/warm frontal rain Extra-tropical cyclone
 …



21

20,000+ pixels for each snapshot

22th IHP Training Course

Nov 22, 2012

Flowchart

22th IHP Training Course

Nov 22, 2012

Raining parameters -1
 DSD assumption

The initial assumption is same as adopted by the PR
operational (2A25) algorithm.
 Allow D0 (medianvolume diameter)
to change +/-3 and+/-6mm aroundthe initial value
when adjusting DSD.


23

22th IHP Training Course

Nov 22, 2012

Flowchart

22th IHP Training Course

Nov 22, 2012

Raining parameters -2
 PR retrievalFind the best-fit CRM profile in
the radar reflectivity (Z) space.

 PIA adjustment

Parallel to the TRMM Radar algorithm.
 Path-integrated attenuation (PIA)
is used when reliable.
 The best solution is sought under
the constraint of PIA by varying
the DSD model.


25

22th IHP Training Course

Nov 22, 2012

Flowchart

22th IHP Training Course

Nov 22, 2012

Identifying rain-free TMI FOVs
Find PR pixels located within a given TMI 19-GHz FOV.
 If none of thesePR pixels contains
a rainfall signal,
the TMI footprint
is defined as"rain free".


27

22th IHP Training Course

Nov 22, 2012

Flowchart

22th IHP Training Course

Nov 22, 2012

Nonraining parameters
Retrieve nonraining parameters (water
vapor, surface wind, cloud water, andSST) for rain-free TMI footprints.
 WV and CLW are also derived forraining PR pixels from PR-matched
CRM profiles.
 The complete fields of the non-raining
parameters are obtained by spatial
interpolation.


29

22th IHP Training Course

Nov 22, 2012

Flowchart

22th IHP Training Course

Nov 22, 2012Tb computations -1
 Compute brightness temperatures

The 3-D structure of all the raining and nonraining
parameters are available from the previous steps.
 Microwave brightness temperature is computed forcomparison with TMI measurements.
 A slant TMI sight line intersecting
neighboring columns is taken into
account.
 Beam convolution is applied witha 2-D Gaussian beam pattern.


31

22th IHP Training Course

Nov 22, 2012Tb computations -2
PR-retrieved precipitation profiles exhibit biases in thecomputed Tb with the initial assumptions.

f ice



22th IHP Training Course

Nov 22, 2012

Flowchart

22th IHP Training Course

Nov 22, 2012

Updating assumptions -1
 Discrepancy in brightness temperatures

A larger (smaller) D0 results in a colder (warmer) Tb in
the emission channels through the underestimation(overestimation) of rain water.
 A higher (lower) ice-particle density (or fluffiness) results
in a colder (warmer) Tb in the scattering channels.
 Adjustment of DSD and ice-density models
34

Modify D0 and fice (relative factor multiplied to theoriginal ice-density model) and iterate the retreival.

22th IHP Training Course

Nov 22, 2012

Updating assumptions -2
DSD and ice-density models are adjusted so that the bias
in Tb is minimized.

f ice



22th IHP Training Course

Nov 22, 2012

Global application - PR results

Correlation with the PR operational (2A25) rain

22th IHP Training Course

Nov 22, 2012

Global application - TMI results

Statistics of computed/observed Tbs

22th IHP Training Course

Nov 22, 2012

Global application - TMI results

RMS error in Tb before/after updating assumptions.

22th IHP Training Course

Nov 22, 2012

Global application - DSD modelD0 as a function of surface liquid water content

PR PIA adjusts DSD only forheavy rainfall.
 TMI Tb adjusts DSD over theentire range of rainfall.


22th IHP Training Course

Nov 22, 2012

Summary – algorithm description
 Combined radar and radiometer algorithm

The radar module retrieves a full 3D rainfall structure
but is susceptible to uncertainties in DSD assumptions
 The radiometer module helps constrain the rain rate
uncertainties in the radar retrieval.


Radar Module
Detailed profiling of
precipitation profiles

Radiometer Module
Constrains uncertainties
underlying the radar
retrieval

22th IHP Training Course

Nov 22, 2012

Toward future applications

22th IHP Training Course

Nov 22, 2012

Global Precipitation Measurement (GPM)© NASA
22th IHP Training Course

Nov 22, 2012

Combined algorithm in the GPM era
 Combined radar and radiometer algorithm

The radar module retrieves a full 3D rainfall structure
but is susceptible to uncertainties in DSD assumptions
 The radiometer module helps constrain the rain rate
uncertainties in the radar retrieval.


 GPM


Consists of the core observatory and multiple
constellation satellites.
 Only the core satellite carries both radar and radiometer.
 Other satellites have radiometers or sounders.



A combined algorithm runs with the core observatory.
 Core satellite retrievals improves the constellation rainfall

algorithm through an a prior database.
22th IHP Training Course

Nov 22, 2012

Flowchart

Rainfall databasefrom the GPM
core observatory

22th IHP Training Course

Nov 22, 2012

Global Precipitation Measurement (GPM)

Core satellitewith combinedalgorithm
A priori
database of
precipitation© NASA45

22th IHP Training Course

Nov 22, 2012

Precipitation Measurement from Space and its Applications

Ground Validation of Rain Data (1)Hiroshi Uyeda
Hydrospheric Atmospheric Research Center, Nagoya University

The Twenty-second IHP Training Course

23 November 2012

in NagoyaIntroduction
Tropical Rain Measuring Mission (TRMM) has been providing rain data
since 1998 for tropical region and subtropical region. TRRM rain data intropical sea with little rain data is valuable. Even on land, TRRM rain data isvaluable for understanding characteristics of rain in the region where less rain
gauge data and weather radar data are available.
TRMM daily rain data as 3B42 provides climatological characteristics ofrain in the region where less rain gauge data is available. Recent
development of Doppler radar observation in developing countries stillrequires past data of rain for understanding the current variation of rainfalls .
Studies on characteristics of rain revealed by comparison of TRMM data
and rain gauge data would be a good example of study on characteristics ofrain. Comparison of rain gauge data and TRMM rain data over Bangladesh
will be introduced.
TRMM 3B42 underestimates rainfall amount in the moist region during themonsoon period. This is related to the not tall convection in the moist
environment. Analyses of TRMM rain radar data revealed that not tallconvections provide large amount of rain even the cloud height is relatively2
low comparing with thunder clouds in pre-monsoon in Bangladesh.

Introduction
Understanding the vertical structure of precipitation systems, dynamical
characteristics of severe storms will be next target to study with Doppler radar.
Recent development of Doppler radar network in Bangladesh and preliminary
results obtained by the Bangladesh Meteorological Department Doppler radar
will be introduced. Numerical simulation of the fast moving system observedby the Doppler radar will be introduced.
Furthermore, X-band polarimetric radar network of Ministry of Land,
Infrastructure Transport and Tourism, Japan will be introduced briefly showing
a heavy rain case, in order to have a view on next generation radar.
Through these works merit of TRMM measurement of rain and expectation
to GPM will became clear.

First comparison of weather radar and TRMM PR was made in
GAME/HUBEX (Huaihe River Basin Experiment).

20JST 29 June 1998

The Twenty-second IHP Training Course

08JST 2 July 1998

23 November 2012

in Nagoya

The Twenty-second IHP Training Course

前坂 剛

23 November 2012

in NagoyaGAME/HUBEX (Huaihe River Basin Experiment)

1607-1610 BST 29 JUN. 1998

The Twenty-second IHP Training Course

23 November 2012

in NagoyaRainfall in Bangladesh as an Example
Characteristics of TRMM derived rainfall in the SAARC region:
Preparation to utilize GPM data in South Asia

Hiroshi Uyeda
andRafi Uddin (BUET )Md. N. Islam (SMRC)

The Twenty-second IHP Training Course

23 November 2012

in NagoyaTarget Area
SAARC (The South Asian Association
for Regional Cooperation )Bangladesh, Bhutan, India, Maldives,
Nepal, Pakistan, Sri Lanka andAfghanistan.

Map of the SAARC region.
The Twenty-second IHP Training Course

The SAARC region has unique characteristics
that there is a large geographic variation
within a short range.
The spatiotemporal distribution of rainfall in
the SAARC domain varies in different rainy
periods.
Utilization of TRMM data for understanding
the rainfall climatology and interseasonalvariation of monsoon rainfall in this region isimportant.
Long-term TRMM products in estimating exact
rainfall are the preparation to utilize GPM
products in different sectors including climatechange impact studies, agriculture and water
management.
23 November 2012

in NagoyaResearch Group
PI: Hiroshi Uyeda
Hydrospheric Atmospheric Research Center,
Nagoya University
CI-1: Md. Nazrul Islam
Synoptic Division,
SAARC Meteorological Research Centre,
Dhaka-1207, Bangladesh→King Abdulaziz University Jeddah, KSA
TRMM Data Analyses(Precipitation )Rain Gauge Data
(SAARC Area )BMD Doppler Radar
Data Analyses
SAARC: South Asian Association
for Regional Cooperation
BMD: Bangladesh Meteorological Division
The Twenty-second IHP Training Course

CI-2: Md. Rafi Uddin
Department of Physics,
Bangladesh University of Engineering& Technology,
Dhaka-1000, Bangladesh
CI-3: Md. Abdul Mannan
Storm Warning Centre
Bangladesh Meteorological Department
Agargaon, Dhaka-1207, Bangladesh.
CI-4 : S. M. Quamrul Hassan
Storm Warning Centre
Bangladesh Meteorological Department
Agargaon, Dhaka-1207, Bangladesh.
23 November 2012

in Nagoya

Characteristics of Rainfall in Bangladesh

Rain Gauge and Dhaka Radar
The triangles (left panel) and cross (right panel) represent the rain-gauge location in Bangladesh.
Square box represents radar locations. Gray shade represents elevation in m.
The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Characteristics of Rainfall in Bangladesh

Rain-gauge rainfall at Dhaka station in 2000 in different rainy periods.
The Twenty-second IHP Training Course

23 November 2012

in NagoyaPoint-to-point comparison of Daily Rainfall (mm) determined byRain-gauge (left of + mark) and TRMM-3B42 (right to + mark) (Data used: March-Nov)
Rainfall (mm/day)

25

24
8.6 9.123

6.2 9.2

6.0 8.5

90

91

92

16.8 8.1

88

93

89

90

Rangpur98

Teknaf00

Teknaf99

Teknaf98

In average Rain-gauge=10.43 mm
TRMM=9.13%=87.47

Khulna00

Khulna99

TRMM

91

92

93

Longitude (E)23 November 2012

Sylhet00

89

Bay of Bengal

Longitude (E)Khulna98

Dhaka00

The Twenty-second IHP Training Course

23

21

17.0 10.4

88

Rain-gauge

Dhaka98

18
16
14
12
10
8
6
4
2
0

Bay of Bengal

21

Longitude (E)Dhaka9989

7.7 8.7

Sylhet99

Tekna 15.4 8.5
90 f
91
92
93

24

Sylhet98

Bay of Bengal
21

16.2 10.1

22

22

22

88

25

12.1 11.1

Rangpur00

Khepupar6.7 9.1aRainfall (mm/day)

Latitude (N)8.2 10.1

6.0 7.4

Rangpur99

Rangp
ur
Dhaka
24
25

2000 Rainfall (mm/day)26

10.7 7.9

Sylh
et15.9 10.8
Latitude (N)8.6 8.8

23

1999 Rainfall (mm/day)

26

Latitude (N)

1998
26

in NagoyaRNG_98-02

12

TRMM_98-02

Rainfall (mm/day)

10
8
6
4
2
0
Wet-region

Dry-region

Fig. 11. Rainfall (mm/day) averaged for the wet region (high humidity) and the dry region(low humidity). Data averages for 17 stations in the wet region and 14 stationsin the dry region throughout Bangladesh from 1998 to 2002.

M.N. Islam, H. Uyeda / Remote Sensing of Environment 108 (2007) 264 –276
The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Characteristics of Rainfall in Bangladesh

Distribution of the locations ofmaximum rain rates in April (darkclosed circle), July (medium darkclosed circle), and October (lightdark closed circle). The open circle
at the lower left of the panel
represents the size of rain rate.

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Characteristics of Rainfall in Bangladesh
7

Observed 15StAve

TRMM 15StAve

0

Bias

6

-10
-20

4
-30
3

Bias (%)

Rainfall (mm/d)

5

-40

2
1

-50

0

-60
1998

1999

2000

2001

2002

2003 2004
Year

2005

2006

2007

98-07

Annual rainfall and bias obtained from TRMM andrain-gauge data over Nepal. Rainfall and bias areaveraged for 15 stations.
The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Characteristics of Rainfall in Bangladesh
20

RNG

18

TRMM

16

Rainfall (mm/day)

14
12
10
8
6
4
2

Teknaf-02

Teknaf-01

Teknaf-00

Teknaf-99

Teknaf-98

Sylhet-02

Sylhet-01

Sylhet-00

Sylhet-99

Sylhet-98

Rangpur-02

Rangpur-01

Rangpur-00

Rangpur-99

Rangpur-98

Khulna-02

Khulna-01

Khulna-00

Khulna-99

Khulna-98

Dhaka-02

Dhaka-01

Dhaka-00

Dhaka-99

Dhaka-98

0Fig. 4. Rainfall (mm/day) measured from TRMM 3B42 and rain-gauge (RNG)
data for a 5-year (1998-2002) period at each selected station.
M.N. Islam, H. Uyeda / Remote Sensing of Environment 108 (2007) 264–276
The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Characteristics of Rainfall in Bangladesh

Seasonal cycle of rainfall in Bangladesh obtained from TRMM products and rain-gauge.

17

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Characteristics of Rainfall in Bangladesh (TRMM PR)

TRMM 2A25 overpass on 25 May 2002 (left panel), Dhaka radar PPI scan (middle panel),and vertical extension of 2A25 precipitation field (right panel) along line AB in left panel.

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Characteristics of Rainfall in Bangladesh (TRMM PR)

Same as the previous figure except for the case in July 2001.
The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Characteristics of Rainfall in Bangladesh (Rain Gauge + TRMM)

Rain Gauge + TRMM
TRMM under estimates
rainfall amount in the
moist region.

Daily rainfall averages from
1 March to 31 November for
1998-2002 at rain-gauge sites(plus mark).

The Twenty-second IHP Training Course

23 November 2012

in NagoyaStatus data of rain rate [BMD Radar]

Status1
2
3
4
5
6

The Twenty-second IHP Training Course

Rain Rate (R)(mm/h)
1≤R<4
4 ≤ R < 16
16 ≤ R < 32
32 ≤ R < 64
64 ≤ R < 128
R ≥ 128

23 November 2012

in Nagoya

Characteristics of precipitation systems [BMD Radar]
Precipitation systems are classified as Arc-, Line- or Scatteredtypes according to their shape by analyzing six years (20002005) radar data of Bangladesh Meteorological Department(Rafiuddin et al., 2007: MSJ autumn meeting, Sapporo).

1. Arc type

2. Line type

Having arc shape leadingedge and behind has
stratiform region (it looks likesquall line type)

Having linear shape echo
Not well organized small(sometimes having linear
individual echoes distributed
echo in-between week echoes) in certain area

Arc typeLine type

3. Scattered type

Scattered type

Scattered systems
Scattered systems have wide areal coverage (SWAC) anddevelopment of this type of system is ~97% in monsoon.

SWAC

Scattered systems

23

Data number analyzed

Interannual variation of the occurrence number of systems types indifferent years (2000-2005)

The Twenty-second IHP Training Course

23 November 2012

in NagoyaSchematic illustration of pre-monsoon andmonsoon precipitation systems

Pre-monsoon

MonsoonSchematic illustration of pre-monsoon (left panel) and monsoon (right panel) precipitation
systems developing in and around Bangladesh. The symbols A, L, S, and SW represent
arc-, line-, scattered-type systems, and SWAC, respectively.
The Twenty-second IHP Training Course

23 November 2012

in NagoyaRAINY SEASON(98%) MarchNovember
DRY SEASON(2%) DecemberFebruary

RAINFALLSEASON

Rainfall and Precipitation Type
PRE-MONSOON (20%)
March-MayMONSOON (62%)
June-September

Systems have Short
Length, Short Lifetime,
Intensified, Fast Moving

Systems have Long Length,
Long Lifetime, Less-intensified,
Slow Moving

Post-MONSOON (16%)
October-November

System characteristics
are almost same as
MonsoonAbove definitions to subdivide Rainy Season are
based on Month. Sometimes Monsoon ONSET iseither Delayed or Prompt from Normal. Therefore,
we are interested to divide Rainy Season basedon the characteristics of Convection Systems.
Also for Model Parameterization vertical profilesof Convection Systems developed in different
rainy periods are important.
Islam and Uyeda (2005), IEEE 2 nd TRMM Con., Korea

Data to be analyzed
YEAR

RADAR

1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012

N/A
N/A
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON

COX’S BAZAR
RADAR
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON

MAULOBI BAZAR
RADAR
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON

RAINGAUGE
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON

TRMM DATAMAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON
MAMJJASON

The data collected and to be collected for the periods.
The Twenty-second IHP Training Course

23 November 2012

in NagoyaSummary◎ We would extend our studies on types of….precipitation systems over Bangladesh by…. ….
….using BMD Doppler radar and TRMM data.
◎ TRMM data, rain gauge data and BMD Doppler…. radar data will be used to reveal characteristics…. of rainfalls over SAARC Area.
◎ These studies would contribute to utilize GPM ….
…. products.

The Twenty-second IHP Training Course

23 November 2012

in NagoyaTopography around Bangladesh and radar coverage

TIBETAN PLATEAUNEPAL
BHUTANINDIA
BANGLADESH
INDIA
Radar
coverage

BAY OF BENGALMYANMAR
Locationof RadarModel Description
18z

JRA-25 (1.25 degree)

00z

06z

12z

18z

00z

06z

27 APR 2002

26 APR 2002

42 hrsMM5 (45 km)
MM5 (15 km)
MM5 (5 km)
dx= 45 km(61 x 58 )(142 x 130)(289 x 247)
Simple ice
Grell
MRF

Nepal

Tibetan Plateau dx= 15 km
dx= 5 kmBhutanIndia

Bangladesh
Radar coverage

Myanmar

Domain 1 (D1)
Domain 2 (D2)
Domain 3 (D3)
Moisture scheme
Cumulus scheme
PBL

12z

D3, MM5
D2, MM5

JRA-25
D1, MM5

Bay of Bengal

2. Model Description
MM5 design for simulation
Number of domain

3 (D1, D2 & D3)

Domain 1 (D1)(61 x 58 )Domain 2 (D2)(142 x 130)

Domain 3 (D3)(289 x 247)

Horizontal grid size

D1: 45 km, D2: 15 km & D3: 5 km

Moisture scheme

Simple ice

Cumulus scheme

Grell

PBL

MRF

Integration time

42 hours

Initial & boundary

JRA-25 (1.25 degree, 6 hrly)

The Twenty-second IHP Training Course

23 November 2012

in NagoyaRadarResults and Discussion (Arc type)

MM5

Initial Stage

Mature Stage

Decaying StageResults and Discussion (Storm motion)

Radar

MM5
22 m/s8m

18 m/s/s8/sm

The Twenty-second IHP Training Course

23 November 2012

in NagoyaResults and Discussion (T, Wind, Ref., at 925 hPa)
26 APR 2002 1900 LST

26 APR 2002 2200 LST

27 APR 2002 0000 LST

Results and Discussion (RH, Wind, Ref., at 925 hPa)
26 APR 2002 1900 LST

26 APR 2002 2200 LST

27 APR 2002 0000 LST

35Results and Discussion (Environment ahead Arc)

26 APR 2002 1900 LSTθθeHeight (hPa)θesTemperature (K)36
The Twenty-second IHP Training Course

23 November 2012

in NagoyaResults and Discussion (Environment ahead Arc)

θ
θe
θes

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Conclusions
 Shape and southeast-ward propagation of the
Arc type system are simulated well by MM5.

 Simulatedcharacteristics (shape, length,
propagation speed and direction) are very close
to radar observation.

 New and intense cells of the asymmetric squallline are developed
southwestern region.

in

high

temperature The low level (~ 1 km) southwesterly (moist air

from Bay of Bengal) has an important role forthe development of new cells.

 Strong outflow from the stratiform region(northeastern part ) makes the system move fast.
The Twenty-second IHP Training Course

23 November 2012

in NagoyaFuture works
 Vertical structure of the systems:
Cloud resolving model (1 km)
BMD Doppler radar
TRMM - GPM
 Understanding the scattered type:
Cloud resolving model (1 km)
Polarimetric radar observation
 Evaluation of GPM precipitation type:
BMD Doppler radar will be useful.
CReSS 1 km run:
"Development of an Arc-Shaped Precipitation System
During the Pre-monsoon Period in Bangladesh"by Rafiuddin, H. Uyeda and M. Kato
Journal of Meteorology and Atmospheric Physics (2012)
The Twenty-second IHP Training Course

39
23 November 2012

in Nagoya

(MAP-D-12-00042R1)"Development of an Arc-Shaped Precipitation System
During thePre-monsoon Period in Bangladesh"

Simulation by using Cloud Resolving Model,
CReSS (Cloud Resolving Storm Symulator)

Rafiuddin et al.
2012

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

The Twenty-second IHP Training Course

23 November 2012

in NagoyaFig. 8 Time series of cell development observed by radar.
The Twenty-second IHP Training Course

23 November 2012

in Nagoya

The Twenty-second IHP Training Course

23 November 2012

in NagoyaCoverage Area of BMD’s Radar in Bangladesh
No. of Radar in Bangladesh: 5(Doppler = 3, Convectional = 2)
To study the structure of
precipitation system TRMM
PR data will be used.

RANGPURMOLVI BAZAR
BAZAR
MOLVI
DHAKA
DHAKA

(Doppler)(Doppler)

KHEPUPARAKHEPUPARACOX’S BAZAR
BAZAR(Doppler) COX’S(Doppler)(Doppler)(Doppler)

In order to understand thetime variation of precipitation
system, Cox ’ s Bazar Doppler
radar data will also be usedwith TRMM PR data.

Cox’s Bazar Doppler Radar Specifications
Radar Type: S-band weather radar

Location: 91.9764 E, 21.4340 N

Radar Height: 117 meter

Frequency: 2847 MHz ( ~3 GHz)

PRF1

: 720 Hz

PRF2

: 576 Hz

Rotational Speed: 6 Deg/s(24 deg/s for long range)
Elevation angle : 0, 1.5, 3, 4.5, 6, 9, 12, 15 Deg(0, 1.5, 3 deg for long range)
Radar Range: 200 km(for long range 440 km)

Operation Hour : 02-03 UTC, 05-06 UTC,
08-09 UTC, 11-12 UTC

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Case of 5 June 2007

The Twenty-second IHP Training Course[TRMM PR]

23 November 2012

in Nagoya

Case of 5 June 2007

TRMM PRThe Twenty-second IHP Training Course[TRMM PR and Cox’s Bazar Radar]
Cox’s Bazar Doppler radar

23 November 2012

in Nagoya

Case of 5 June 2007

[TRMM PR: Vertical Cross Section]

C
A B

B’
A’
C’

A

The Twenty-second IHP Training Course

A’

B

B’

C

C’

23 November 2012

in Nagoya

Case of 5 June 2007

[Reflectivity (PPI)]Radar

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Case of 5 June 2007

[Reflectivity (PPI)]Radar

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Case of 5 June 2007

[Reflectivity (PPI)]Radar

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Case of 5 June 2007

[Tracking of Convective Line]

05 JUN 2007

z
:07
0
0
:
02
z
07 6z
:
5
:4
:0
02
00
:
03

Radar

06
:1
5:
06
z

z
06
:
0
:0
5
0

Propagation speed of system = ~ 18 m/s

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Case of 5 June 2007

[Reflectivity (CAPPI)]

05

Radar

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Case of 5 June 2007

[Doppler velocity (CAPPI)]

05

Radar

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Case of 5 June 2007

The Twenty-second IHP Training Course[Reflectivity (Vertical Cross-section)]

23 November 2012

in Nagoya

Case of 5 June 2007

The Twenty-second IHP Training Course[Velocity (Vertical Cross-section)]

23 November 2012

in Nagoya

Case of 5 June 2007

The Twenty-second IHP Training Course[Velocity (Vertical Cross-section)]

23 November 2012

in NagoyaPerformance of X-band Polarimetric Radar Network
for Estimation of Rainfall Intensity andUnderstanding Mechanism of Heavy Rainfall

Progress for Future

The Twenty-second IHP Training Course

23 November 2012

in NagoyaRadar Network in East Asia

~ 0615 UT
12 August 2012

The Twenty-second IHP Training Course

23 November 2012

in NagoyaDoppler Radar → Polarimetric (MP) Radar
Recently, X-band polarimetric radar network has been constructed inJapan for monitoring and prediction of heavy rainfall in urban area.
Performance of X-band polarimetric radar for estimation of rainfall intensity
and identification of precipitation particle type has been verified.
Progression:
Research in Japan・Dual-polarization Radar
1987 C-band Public Works Research Institute1991 X-band Hokkaido University
1995 X-band NIED・Polarimetric Radar (measures Phase Shift)2000 X-band (W-band) NIED2001 C-band CRL (present NICT) [COBRA]
2007 X-band Nagoya University
For operational use in Japan
2010 X-band MLIT ( Ministry of Land, Infrastructure, Transport and Tourism ) [X-RAIN]* Main target is heavy rainfall in urban area.
The Twenty-second IHP Training Course

23 November 2012

in NagoyaHyARC, Nagoya University, X-band Polarimetric (MP) Radar

The Twenty-second IHP Training Course

Frequency

9375 MHz, 9415 MHz

Band width

4 MHz

Amplifier

Solid-state

Peak power

200 W

Pulse width

1 μs (short pulse)
1～64 μs (long pulse)

PRF

2000 Hz (normal)
20000 Hz

Antennadiameter

2.0 m

Beam width

1.2 deg.

Observation
parametersρHV(0),
ZH, v, σv, ZDR,ΦDP, KDP LDRHV，LDRVH

Observation
range

64 km
23 November 2012

in NagoyaPolarimetric Radar
Illustrate by Kouketus

PolarimetricParameters

Signal Power：Small
Phase Shift ：Small＜Received Signal Power＞
・Radar Reflectivity（Zh）
・Differential Reflectivity（Zdr）
・Correlation Coefficient（ρhv）＜Phase Shift＞
・Differential Phase Shift（Kdp）

Oblate particle

Signal Power：
Large
Phase Shift ：Largevertically polarized wave

Horizontally polarized wave
polarimetric radar
The Twenty-second IHP Training Course・radarreflectivity
・shape23 November 2012

in Nagoya

65

Polarimetric Parameters（１）：Zh and Zdr●Radar Reflectivity: Zh (=10log10Zh*)●Differential Reflectivity: Zdr

Zh*：reflectivity factor of horizontal wave
D：Diameter N(D)：Particle size distribution

Zh*：reflectivity factor of horizontal wave
Zv*：reflectivity factor of vertical wave
reflectivity factor：small

・Proportional to fifth power of diameter
・Proportional to particle Size distribution

Small

Large

（e.g. drizzle）（e.g. heavy rain, hail）

reflectivity factor：large

・oblate shape → Zdr > 0
・spherical shape → 0・prolate shape → Zdr < 0graupel, haillarge rain drop, ice crystal
drizzle(dBZ)
The Twenty-second IHP Training Course

66 (dB)
23 November 2012

in NagoyaPolarimetric Radar
PolarimetricParameters

Signal Power：Small
Phase Shift ：Small＜Received Signal Power＞
・Radar Reflectivity（Zh）
・Differential Reflectivity（Zdr）
・Correlation Coefficient（ρhv）＜Phase Shift＞
・Differential Phase Shift（Kdp）

Oblate particle

Signal Power：
Large
Phase Shift ：Largevertically polarized wave

Horizontally polarized wave
polarimetric radar
The Twenty-second IHP Training Course・radarreflectivity
・shape23 November 2012

in NagoyaPolarimetric Parameters（２）：ρhv and Kdp●Correlation Coefficient: ρhv●Differential Phase Shift: Kdp

Φdp： differential phaser ：range

sh：signal power of horizontal wave
sv：signal power of vertical wave

phase shift：small

・homogeneous shape → high (～1.0)
・inhomogeneous shape → low
・wet (melting) ice → very low (<0.95)※low SNR → low value

Phase shift：large

・oblate particle → Kdp is large.
・Large water content → Kdp is large.

drizzlewet snow, hail

rain
snow

drizzleHeavy rain

68
(deg/km)
The Twenty-second IHP Training Course

23 November 2012

in NagoyaX-band MP Radar Network (Nagoya Area) of WDMB, MLIT
MILT (Ministry of Land, Infrastructure, Transport and Tourism)
Heavy Rainfall Case (2008/8/29)

System M

ovement

Cell Movement
Cell Generation Area

Population Density

Moist AirThe Twenty-second IHP Training Course：Coverage of 3 Radar：Coverage of 2 Radar： Radar Site

in 2010
Tokyo Aria (2)Nagoya Area (3)Osaka Area (4)Hokuriku Area (2)in 2011
13 more MP Radar
69
23 November 2012

in NagoyaX-band polarimetric radar network of MLIT

C-bandRadarcoverage

The Twenty-second IHP Training Course:
Area of X-band
MP RadarNetwork

23 November 2012

in NagoyaAn Example of heavy rainfall in Nagoya Area
MLIT X-band MP Radar○ KANI♢ NAGOYA

m

Range

80km

Mode

PPI (12EL, 5 min.)Resolution

150m (in radial)
1.2°（Beam width）

Observation Parameters・Zh : Reflectivity
・ Zdr : Differential Reflectivity
・Φｄｐ： Differential Phase
・Kdp : Differential Phase Shift・ρｈｖ ： Correlation Coefficient：X-band MP Radar (at Bisai)：X-band MP Radar (at Suzuka)：X-band MP Radar (at Anjo)
Heavy Rainfall in Kani:
300 mm in half a day on 15 July 2010
The Twenty-second IHP Training Course

・Doppler Velocity
3D Cartesian
Coordinate

Hoizontal Resolution
Vertical Resolution

1 km
250 m

23 November 2012

in NagoyaEstimation of Rainfall Intensity and Hourly Rainfall AmountRiver Bureau (Ministryof Land, Infrastructure,
Transport and Tourism)
X-RAIN Data
Research Consortiumfor X-RAIN

Volume Scan Data (every 5 minutes)
CAPPI at 1 km (1 km × 1 km × 250 m)<For Weak Rainfall>
ZDR ～0 (dB), KDP～0 (deg/km), ZH > 0 (dBZ)

Z = BRβZ (mm6m-3 ) , R (mm/h)
B and β are constants
ZDR : Differential Reflectivity
KDP : Specific Differential
Phase<For Strong Rainfall>
ZDR > 0.5 (dB), KDP > 0.5 (deg/km),
ZH > 40 (dBZ)

R = c (KDP)a × 10(0.1×b×ZDR)

Accumulated rainfall amount (Hourly Rainfall)(Use 12 planes at 1 km)
The Twenty-second IHP Training Course

23 November 2012

in NagoyaAccumulated Rainfall Amount○

HAMAMATU

Accumulated rainfall amount from 15 to 24
on 15 July 2010 estimated by MILT X-band
MP radar.

The Twenty-second IHP Training Course

23 November 2012

in Nagoya

Time Variation of the Precipitation System
1600 JST- 2330 JST
15 July 2010

JMA Radar

The Twenty-second IHP Training Course

23 November 2012

in Nagoya１、はじめに Estimation of Rainfall Intensity
Convective Region（Strong Rainfall）
Threshold (MLIT)

Threshold （This Study）

Kdp ≧ 0.1 deg/km

Kdp ≧ 0.1 deg/km

Zh ≧ 30 dBZ

Zh ≧ 25 dBZ

R = 25.5 ×Kdp0.815 ÷ 12

（mm/5min）

Stratiform Region（Weak Rainfall）
Bringi and Chandraseker (2001)
R = 0.0365 × Z0.625÷ 12（mm/5min）

Z = 10 0.1×Zh

Height : 1 km

Rainfall in 5 min.
Calculate for each grid
(1 km x 1 km x 0.25 km)

The Twenty-second IHP Training Course

23 November 2012

in NagoyaThreshold of Zh in Estimation of Rainfall Intensity with KDP
１、はじめにThe threshold of Zh ≧ 30 dBZ makes under estimationof rainfall intensity without using KDP.
Zh ≧ 30 dBZ

Zh ≧ 25 dBZ

Zh = 28 dBZ
The Twenty-second IHP Training Course

23 November 2012

in NagoyaScatter Diagram of of Zh and R (estimated with KDP)
１、はじめに77
The Twenty-second IHP Training Course

23 November 2012

in Nagoya１、はじめに

Evaluation of the Method

RAINFALL 1800-1900 JST 15 JUL 2010

TajimiRAINFALL 1800-1900 JST 15 JUL 201042.5 mm

8 mm

Tajimi80.5 mm

RAINFALL (mm)

mm

1 mm

TIME

1 hour
Accumulated
Rainfall： MP radar： Rain gauge

The Twenty-second IHP Training Course

83 mm
80.5 mm

23 November 2012

in Nagoya

Identification of Cell and Cell Group
１、はじめにExtraction of Cell
4 mm/5 min

Extraction of Cell Group

Center of Cell

4 mm/5 min

Ｃ
Cell

5 km
10 min.

Cell Group
20 min.

5 km

Draw contours every 4 mm/5 min↓
Identify a cell with maximum
rainfall intensity.

20 min.

1 Cell Group
2 Cell Group

Identification was made by eye.
79
The Twenty-second IHP Training Course

23 November 2012

in Nagoya１、はじめに Identification of Cell Group
RAINFALL 1600-1700 JST 15 JUL 2010

S1

18km
mm

50 km
90km

S2

39 mm

RAINFALL (mm)

45 mm

10 km

TIME
The Twenty-second IHP Training Course

20 km

mm
23 November 2012

in NagoyaAccumulated Rainfall Amount（Stationary System S）

S2

S1

Tracking and
Accumulation
Mehod

11 km

35 km

13 km

45 km
10 km

S4

Max.： 84 mm

13 km

89 km

Max.： 90 mm
The Twenty-second IHP Training Course

S5Max.： 85 mm

17 km

36 km

Max.： 85 mm

20 km

S : Stationary

S6

S3 was not tracked.
11 km

43 km

Max.： 52 mm
23 November 2012

in NagoyaAccumulated Rainfall Amount（Fast Moving System F）
１、はじめに

F2

F1

F3

15 km

40 km

Max. ： 78 mm

13 km

33 km

Max. ： 49 mm

12 km

45 km

Max. ： 51 mm

F : Fast moving

The Twenty-second IHP Training Course

23 November 2012

in Nagoya１、はじめに Characteristics of Cell Group
Cell
Group
S1
S2
S3
S4
S5
S6
F1
F2
F3

Max.
Max. Rain
NumberDuration （min.）Rain
in Cellof Cell(mm)(mm)
15:30～17:00 (95) 11
35
0.31
84
4
45
16:00～17:55 (120) 13
45
0.29
85
8
39
15:50～17:55 (75)
*
*
*
*
5
*
17:15～19:10 (240) 12
89
0.13
90
9
36
18:00～19:55 (120) 17
36
0.47
85
10
38
19:15～21:10 (120) 11
43
0.26
52
5
23
20:20～22:00 (105) 15
40
0.38
78
4
69
21:30～22:50 (85) 13
33
0.39
49
4
16
21:45～23:25 (105) 12
45
0.27
51
5
43
Characteristics of Cell Group(Accumulated Rainfall Amount > 20 mm)
Width Length Aspect(km) (km) Ratio

Elongated
Shape
The Twenty-second IHP Training Course

Width ： 11～17 km
Orientation： SW - NE
Length： 33～89 km
23 November 2012

in NagoyaFormation Process of Heavy Rainfall Area
１、はじめに
1500-2400

13 km: 322 mm
39 km

Accumulated
Rainfall Amountin 9 hours― ： 200 mm--- ： 250 mm and 300 mm

The Twenty-second IHP Training Course

23 November 2012

in NagoyaFormation Process of Heavy Rainfall Area
１、はじめに
mm
その他
9%
F3

10%

F2

8%

S6

15%

S5

17%

S4

1%

S2
Contribution= Rainfall amount of each Cell Group
× 100
Rate
Max. of Accumulated Rainfall Amount

S1

20%

20%

△: The point of maximum rainfall amount

S1～S6 : 50 mm
F1～F3 : 40 mm

The Twenty-second IHP Training Course

23 November 2012

in NagoyaFormation Process of Heavy Rainfall Area
１、はじめに
mm
その他
9%
F3

10%

F2

8%

S6

15%

S5
S4

17%

S2
Contribution= Rainfall amount of each Cell Group
× 100
Rate
Max. of Accumulated Rainfall Amount

S1

20%

20%

△: The point of maximum rainfall amount

S1～S6 : 50 mm
F1～F3 : 40 mm

The Twenty-second IHP Training Course

86
23 November 2012

in NagoyaFormation Process of Heavy Rainfall Area
１、はじめに
mm
その他
9%
F3

10%

F2

8%

S6

15%

S5

17%

S4

1%

S2
Contribution= Rainfall amount of each Cell Group
× 100
Rate
Max. of Accumulated Rainfall Amount

S1

20%

20%

△: The point of maximum rainfall amount

S1～S6 : 50 mm
F1～F3 : 40 mm

The Twenty-second IHP Training Course

87
23 November 2012

in NagoyaFormation Process of Heavy Rainfall Area
１、はじめに
mm
その他
9%
F3

10%

F2

8%

S6

15%

S5

17%

S4

1%

S2
Contribution= Rainfall amount of each Cell Group
× 100
Rate
Max. of Accumulated Rainfall Amount

S1

20%

20%

△: The point of maximum rainfall amount

S1～S6 : 50 mm
F1～F3 : 40 mm

The Twenty-second IHP Training Course

88
23 November 2012

in NagoyaFormation Process of Heavy Rainfall Area
１、はじめに
mm
その他
9%
F3

10%

F2

8%

S6

15%

S5

17%

S4

1%

S2
Contribution= Rainfall amount of each Cell Group
× 100
Rate
Max. of Accumulated Rainfall Amount

S1

20%

20%

△: The point of maximum rainfall amount

S1～S6 : 50 mm
F1～F3 : 40 mm

The Twenty-second IHP Training Course

89
23 November 2012

in NagoyaFormation Processes of Heavy Rainfall Area
１、はじめに
mm
Other

9%

F3

10%

F2

8%

S6

15%

S5

17%

S4

1%

S2
Contribution= Rainfall amount of each Cell Group
× 100
Rate
Max. of Accumulated Rainfall Amount

S1

20%

20%

△: The point of maximum rainfall amount

S1～S6 : 50 mm
F1～F3 : 40 mm

The Twenty-second IHP Training Course

90
23 November 2012

in NagoyaFormation Processes of Heavy Rainfall Area
１、はじめにStationary System (1515～2110 JST)
Cell motion

Fast Moving System (2020～2330 JST)

S1

F1

84mm
20%

78 mm
0%

F2
49 mm
8%

S2
85mm
20%

S6
52mm
15%

S3
S5

S4

85mm
17%

90mm
1%

Cell motion

Rainfall per Cell
23 mm～45 mm

Rainfall per Cell
16 mm～69 mm

Contribution Rate：73%

Systemmotion

F3
51 mm
10%

Contribution Rate ：18%

Merit: Identify cell and cell group with rainfall amount in mm.
→ Track in mm and integrate in mm. (Useful for statistical studies.)
Problems: time resolution, attenuation, ground clutter and solid precipitation
91

The Twenty-second IHP Training Course

23 November 2012

in NagoyaOngoing Studies and Research Plan
１、はじめに・Studies on structure of thundercloud・Hydrometeor classification[Pis4-017 Oue]・Assimilation of X-band polarimetric radar data into
cloud resolving model (CReSS)[Pis4-015 Kato]・Statistics of convective cell and precipitation systems
・Improvement of radar system
・Field experiment on Genesis of tropical cyclone in
Palau (2013 – 2014)

The Twenty-second IHP Training Course

23 November 2012

in NagoyaHydrometeor Classification
Zh

Zdr

Input parameters
Kdp ρhv
T (temperature)polarimetric radarsounding, surface

Hydrometeor Classification（Liu and Chandrasekar, 2000）

Output hydrometeor categories
1)Drrizle2)Rain3)Wet (Melting) Snow 4)Dry Snow5)Ice Crystal

6)Dry Graupel 7)Wet (Melting) Graupel8)Small Hail

9)Large Hail

10)Rain and Hail
93

The Twenty-second IHP Training Course

23 November 2012

in NagoyaExample of Hydrometeor Classification

Summer Thundercloud near Nagoya
Identification of graupel!
The Twenty-second IHP Training Course

23 November 2012

in NagoyaCAPPI of Radar Reflectivity (X-band MP Radar of MLIT)

Every 5 minutes volume-scan radar data will provide characteristics ofheavy rainfall associated with typhoon.
The Twenty-second IHP Training Course

23 November 2012

in NagoyaProblems on Heavy Rainfall
Area of
Disaster Strikes

Disastersin recent problem

Expected
Disaster Response

Metropolis

Urban River Flooding
Sewage Overflow
Flooded Underground

Monitoring
Short-term
Forecasting
Warning(Covered by
Monitoring System)

Heavy
RainfallTyphoonCritical Rainfall AmountRiver Basin(50 mm in one hour)
Dam
Critical Rainfall AmountLong-distanceTransportation (～200 mm in one day)Highway
Landslide
Deep-seated
Mountains
Landslide
Landslide
DamAmount
FailureCriticalRainfall(1,000 mm in a few days
and 50 mm in one hour)

Monitoring
Forecasting
Short-term &Long-term
Warning
Utilize
Radar Data

S-band and C-band Doppler Radar + X-band Polarimetric Radar
High-resolution Regional Model + Radar Data Assimilation
The Twenty-second IHP Training Course

23 November 2012

in NagoyaFor Future (Field Experiment in Palau)
TyphoonPressure Drop andEnhancement of Vorticity

Vortex:D ~ 100 ㎞

Observation Year
May & June 2013
December 2013
May & June 2014

Vortex:D ~ 10 km
Vortex: D = 300 ~ 500 ㎞

From Houze (2010)

Aimeliik
Cloud Particle Sonde
Observation

N

60 km

N

Philippine
ＶＨＴ

X-POL

Aimeliik

X-POL

80 km

Palau

Palau

60 km

E
The Twenty-second IHP Training Course

23 November 2012

E
in NagoyaComparison between radar and HYVIS・using RHI data (high resolution in vertical direction)・comparing polarimetric variables (Zh, Zdr, Kdp and ρhv) and particle
type
0.1km

0.5km
Position of HYVIS

polarimetric variables: averaged spatially↑▼The list of comparison

Kouketsu 2012

HYVIS No.

Date of RHI (JST)Height of HYVIS

Temperature

No. 2

16:05 June 1

About 5,800m

-5.5℃

No. 3

21:35 June 2（About 9,400m）（-27.5℃）

No. 5

02:47 June 14

About 9,400m-25.5℃

The Twenty-second IHP Training Course

23 November 2012

in NagoyaData・X-band polarimetric radar (X-pol): installed at Aguni Island
・Hydrometeor Videosonde (HYVIS): launched from Aguni Island●X-pol observation

Okinawa
Aguni Island

Okinawa Island

location

Aguni Island, Okinawa

Observational
periodFrom May 23
to July 13, 2011※

Maximum
range

60km

parameters

Zh, Zdr, Kdp and ρhv

Scan mode

PPI（15 elevations,
every 6 minutes）
RHI（arbitrarily）

range gate

150m

Beam width

1.2°※missing data from May 27 to 29

The Twenty-second IHP Training Course

23 November 2012

in NagoyaHYVISHYVIS = Hydrometero Videosonde
・two types of cameras ： close-up and microscopic cameras

lightcamerasbattery

The Twenty-second IHP Training Course

23 November 2012

in NagoyaHYVIS Observation
X-pol →

↓Monitor of HYVIS↓Antenna of HYVIS

Preparation of launch

The Twenty-second IHP Training Course

Launch of HYVIS

23 November 2012

in NagoyaHYVISHYVIS = Hydrometero Videosonde
・two types of cameras ： close-up and microscopic cameras

Image of Close-up Camera

Image of Microscopic Camera

9mm

1.4mm
102

The Twenty-second IHP Training Course

23 November 2012

in NagoyaResult: HYVIS #2 (June 1, 2011)
Zh (CAPPI: z = 2 km)
X: position of HYVIS

16:00

Zh (RHI: AZ = 26°)
X: position of HYVIS

RHI

16:05

103
The Twenty-second IHP Training Course

23 November 2012

in NagoyaResult: HYVIS #2 (June 1, 2011)
Zdr

16:05

Z ≒ 5,800 m
Particle type: column

0.5mm
X: position of HYVIS

0.38dB

2mm
The Twenty-second IHP Training Course

23 November 2012

in NagoyaThank you !

The Twenty-second IHP Training Course

23 November 2012

in NagoyaGlobal rain map (1)Takuji Kubota
Earth Observation Research Center (EORC)
Japan Aerospace Exploration Agency (JAXA)International Hydrological Programme
Precipitation Measurement from Space and its Applications
The Twenty-second IHP Training Course
22 November 2012 @ HyARC, Nagoya University

Contents
Multi-satellite rainfall Product
Global rain map = Products merged from various sensor data fromsatellites, in addition to other resources (e.g, rain gauges)
Global Precipitation Measurement (GPM)
Global Satellite Mapping of Precipitation (GSMaP)
Brief algorithm flow & Validation

GSMaP application
Cyclone “Nargis”
2011 Thailand floods

GSMaP data handling
How to Get the GSMaP Online Data
Data format
2

Single-satellite products

3

4

Multi-satellite products

5

6

7

Orbit of Various Satellites
TRMM/GPM-core satellites are flying non-sun-synchronous
orbit, which has low inclination angle, in order to observe
diurnal variation of tropical rainfall.
Sun-Synchronous orbit
Observation condition will be
constant at same location
× cannot capture
differences depending on
local time× Least observation
frequencies at equatorial
area

TRMM/GPM-core’s orbit
Local time of observation will bechanging as time go on.
350

km

35°
Equator

36000km

Geostationary orbit
Observe specific region at anytime
× Cannot observe other region
× Horizontal resolution will be worse
8

Global rainfall map
Products merged from various sensor data from satellites, in addition to otherresources (e.g, rain gauges) can be useful for users  Global rain map
Aqua/AMSR-E

DMSP/SSM/I

TRMM/TMIGEO IR

Global Rainfall Map(merged product)

9

Rainfall Measurement from Space
Active sensor

Passive sensor

A remote sensing system that transmit its
own electromagnetic energy, then measures
the properties of the returned radiation.

A remote sensing system that relies on theemission of natural levels of radiation fromthe target.

Radar; Radio Detection And Ranging
Precipitation Radar (PR)TRMM/PR, GPM/DPR

GCOM-W1/
AMSR2
instrument

Global Satellite
Mapping ofPrecipitation(GSMaP)

10

Microwave radiometers (conical scan)SensorSSM/I

TMI

AMSR-E

SSMIS

Windsat

AMSR2

GMI

Satellite

DMSP
series(~F15)

TRMM

Aqua

DMSP
series(F16~)

Coriolis

GCOM
W1

GPM

ProviderDoD, U.S. NASA

JAXA

DoD, U.S. NASA

JAXA

NASA

周波数
[GHz]

19.35 85.5

10.65 –
85.5

6.93 –
89.0

19.35 –
183.3

6.8 – 37.0 6.93 –
89.0

10.65 –
183.3

観測幅
[km]

1400

700

1450

1700

1000

1450

890

Antennasize [m]

0.61

0.61

1.6

0.61

1.8

2.0

1.2

DMSP series

TRMM

Aqua

GCOM-W1

GPM Core
11

AMSU/MHS
AMSU(Advanced Microwave Sounding Unit) is carried by NOAA KLM
satellites (NOAA 15(-K), 1998-; NOAA 16(-L), 2000-; NOAA 17(-M), 2002-)
MHS（Microwave Humidity Sounder）is similar to AMSU-B, and carried by
NOAA 18, 19, Metop-A, Metop-B

Observation swath: 1650km
Footprint size of AMSU-B/MHS:
16km at nadir, but it changes
according to observation directions.)
http://amsu.cira.colostate.edu/

Channel

MSU

AMSU-A

AMSU-B

1

50.30

23.8

89.0

2

53.74

31.4

150.0

3

54.96

50.3

183.3±1

4

57.95

52.8

183.3±3553.6

183.3±7

6

54.4

7

54.9

8

55.5

9

57.2

10

57.29±.217

1157.29±.322±.048

1257.29±.322±.022

1357.29±.322±.010

1457.29±.322±.0045

http://www2.ncdc.noaa.gov/docs/klm/html/c3/sec3-4.htm 15

89.0

(GHz)

12

Operational geostationary satellites
GOES, METOSAT, MTSAT, FY-2, INSTAT, COMS...
carries Infrared (IR) sensor

GEO =Geostationary
Earth Orbit

http://www.wmo.int/pages/prog/sat/globalplanning_en.php
13

Observation of Precipitation from the Satellite
Satellite-borne
microwave radiometer

Satellite-borne
IR radiometer

Raindrop radiation(absorption)
 Snow/ice scattering

Cloud iceparticle

Cloud top
temperaturescattering

Satellite-borne
precipitation radar

Snow/ice
Aggregation ofsnow and ice
Melting layer

radiation

Raindrop scattering

0ºC height

Rain

14

Precipitation characteristics observed by
the space borne sensors
Typhoon MORACOT (8 Aug. 2009)

(a)

(b)

(c)

(d)

(a) Precipitation radar
Back scattering from rain drops
High accuracy
Narrow swath width(b)Infrared radiometer：
Cloud top information
Not related to surface precipitation rates(c)Microwave radiometer(19GHz)：(d)Microwave radiometer(85GHz):
Directly measures emission from rainfall& scattering from snow/ice over the
ocean
Directly measures scattering fromsnow/ice over the land
It is important to combine the data from different frequencies to retrieve precipitation

15

Strength and Weaknesses of Each Sensor
Each sensor has strength and weaknesses.
Microwave passive sensor has
Very good correlation to precipitation
Emission not useful over land
No operational estimates over frozen surfaces
The major draw back is temporal sampling due low earth orbit
satellite (LEO)

Infrared (IR) sensor has
Excellent sampling from Geostationary Earth Orbit (GEO) satellites
Weak instantaneous relationship to precipitation
Weak mean relationship outside 40 degree

 Blended Microwave-IR approach
16

Ground rain gauge distribution
Distribution of rain gauge in GPCC Monitoring Product
Provided by Global Precipitation Climatology Centre (GPCC )http://gpcc.dwd.de17

Weaknesses of Other Approaches
Rain gauge
Wind losses, evaporation, and side-wetting and splashingSparse (in particular) at Tropics
Latency (availability)

Surface-based radar
Small observation area with beam blockage by surface
features
Calibration uncertainties
Anomalous propagation

Numerical prediction model
Difficulty in model representation of precipitation
Initialization errors
18

TRMM impact on global rainfall map

19

Major satellite global rainfall map before
TRMM
Product (organization)

Hori.
res.

Temp. res.

NRT
(delay)Data source andmajor characteristics

GPCP (WCRP/GEWEX)

2.5 deg

1-month×

Merge satellite (IR,SSM/I,TOVS/MSU) with gauge data

1 deg

1-dy

×

Merge satellite (IR,SSM/I, TOVS/MSU) with gauge data

2.5 deg

5-dy

×(1-dy)

Merge satellite (IR,SSM/I, TOVS/MSU) with gauge data.

http://cics.umd.edu/~yin/GPCP/main.html

GPCP 1DD (NASA/GSFC)
http://precip.gsfc.nasa.gov/

CMAP (NOAA/CPC)
http://www.cpc.ncep.noaa.gov/pr
oducts/global_precip/html/wpage.
cmap.html

Main objectives are to obtain global climatology, and temporaland vertical resolutions are coarse.
Calibrated satellite (IR and microwave sensors) data overland by ground-based rain gauges.

20

Major satellite precipitation map after
TRMM (1/2)
Product (organization)

Hori.
res.

Temp. res.

Datalatency

Data source andmajor characteristics

CMORPH/QMORPH(NOAA/CPC)

~8 km

3-hr
(CMORPH)

18-hr

Merging passive microwave sensors (radiometer/sounder)
estimated rainfall & whose features are transported via spatial
propagation information by IR. Not use rain gauge data.

0.5 deg

3-hr(CMORPH)~8 km/
0.25
deg

30-min(QMORPH)3-hr

Real-time version of CMORPH. Using forward propagation
only. Not use rain gauge data.

0.25
deg

1-hr

2-day

Calibration and training IR estimated rainfall using microwave
sensor (radiometer & sounder) rainfall. Not use rain gauge
data.

0.25/0.
1 deg

3-hr

3-hr
(image only)

Blending IR estimated rainfall and passive microwave sensors.
Not use rain gauge data.

4~6 km30-min~3-hr

10-min(local)Regional IR estimated rainfall calibrated by numerical weather
prediction and ground-based radars.

http://www.cpc.ncep.noaa.gov/
products/janowiak/
cmorph_description.html

PERSIANN(UCI/HyDIS)
http://hydis8.eng.uci.edu/
persiann/

NRL Blended (NRL)
http://www.nrlmry.navy.mil/sat-bin/rain.cgi

Hydro-Estimator(NOAA/NESDIS)
http://www.orbit.nesdis.noaa.gov/smc
d/emb/ff/auto.html

21

Major satellite precipitation map after
TRMM (2/2)
Product (organization)

Hori. res.

Temp. res.

Datalatency

Data source andmajor characteristics

TRMM TMPA
3B43/3B42/3B42RT(NASA/GSFC)

0.25 deg

1-month(3B43)

1-monthCombine passive microwave sensors (radiometer/sounder) & IR estimated rainfall. Direct use of monthly
rain gauge.

http://trmm.gsfc.nasa.gov/

0.25 deg

3-hr(3B42)

1-monthCombine passive microwave sensors (radiometer/sounder) & IR estimated rainfall, indirect use of monthly
rain gauge.

0.25 deg

3-hr(3B42RT)

10-hr

Real-time version of 3B42. Not use rain gauge data.

0.25 deg

1-/24-hr(MWR)

Not in realtime

Merging microwave radiometer rainfall using PR indirect
information. Not use rain gauge data.

0.1 deg

1-hr
(MVK)

2-3 days

Combine MWR and whose features are transported via
spatial propagation information by IR, combinational useof Kalman filtering approach. Not use rain gauge data.

0.1 deg

1-hr
(NRT)

4-hr

Real-time version of MVK. Using forward propagation only.
Not use rain gauge data.

GSMaP MWR/MVK/NRT(JAXA)
http://sharaku.eorc.jaxa.jp/GS
MaP/index.htm

higher temporal/horizontal resolution products are available due to improvementin rainfall estimation by microwave radiometer, increase of satellite sensors,
improvement in horizontal resolution
Increase of products distributed in near real-time22

Two types of the global precipitation map
IR-based Approach (Ex. GPCP)
Uses mainly direct conversion of the brightness
temperature at IR wavelength to precipitation rate
Generally, the lower temperature, the higher rain rate
It is generally true

Moving Vector Approach (Ex. CMORPH)
Propagate the rainy pixels with the atmospheric motion
vectorAdvantage: Relatively high score
A possible main source of error of this type of technique isthat the advection vector is the only process that describes
the temporal variation of the precipitation process.

23

Two types of the global precipitation map
IR-based Approach (Ex. GPCP)
Uses mainly direct conversion of the brightness
temperature at IR wavelength to precipitation rate
Generally, the lower temperature, the higher rain rate
It is generally true

Moving Vector Approach (Ex. CMORPH)
Propagate the rainy pixels with the atmospheric motion
vectorAdvantage: Relatively high score
A possible main source of error of this type of technique isthat the advection vector is the only process that describes
the temporal variation of the precipitation process.

24

Global Precipitation Measurement (GPM)
TRMM Era

GPM EraCore Satellite

GPM: An international satellite mission to
be launched by JAXA and NASA in 2014
for precipitation measurements worldwide

Constellation
Satellites

Core Satellite (JAXA, NASA)
Dual-frequency precipitation radar (DPR)
GPM Microwave Imager (GMI)• Precipitation with high precision• Discrimination between rain and snow
• Adjustment of data from constellation
satellites (The core satellite will fly in
non-sun-synchronous orbit.)

Constellation Satellites(International Partners)
Microwave radiometers
Microwave sounders• Global precipitation every 3 hours(launch in 2014)(launch around 2014)
2 satellites/3hr

 Improve the accuracy of both long-term andshort-term weather forecasts
 Improve water resource management in rivercontrol and irrigation systems for agriculture

8 satellites/3hr

25

“DPR” aboard GPM core satellite(launch in 2014)
Flight direction

Dual-frequency precipitation
radar (DPR) consists of-Ku-band (13.6GHz) radar : KuPR
(similar to TRMM/PR)
and-Ka-band (35.5GHz) radar : KaPR

The DPR wasdeveloped by
JAXA and NICT.

GMI
DPR

407 km altitude,
65 deg inclination

GMI: GPM Microwave Imager

Range resolution= 250m and 500m

KuPR (13.6GHz)
Swath width = 245km

5km

KaPR (35.5GHz)
Swath width = 120km

Microwave radiometer
Swath width = 800 km

26

Animation of GPM Core satellite

27

Photo of DPR instruments

28

Current status
Mechanical and Electrical integration to GPM
has finished in May 2012.
Comprehensive Performance
Test (CPT) is in progress.

Credit: NASA29

Role of DPR in GPM mission

GPM core satellite and TRMM are flying non-sunsynchronous orbit, while Constellation Satellites are flying
in non-sun-synchronous orbits.
30

Multi-satellite rainfall product by JAXA (GSMaP)As a prototype of JAXA GPM product, we have started to release hourly
global rainfall data (0.1x0.1deg. lat/lon) in near real time (about four hours
after observations) and visualize the latest data quickly.
Aqua/AMSR-E

Global Satellite Mapping of Precipitation (GSMaP)DMSP/SSM/I

TRMM/TMIGEO IR

produced 4 hours
after observation andupdated every hour

Global Rainfall Map(GSMaP_NRT)

http://sharaku.eorc.jaxa.jp/GSMaP/

Internet access• Images & Movies• Google Earth files• Data download

31

Production of GSMaP by Multi-satellite Data

NOAA
AMSU-B
TRMM
TMI

Aqua
AMSR-EDMSP
SSM/I, SSMIS

GSMaPMicrowave Radiometer (MWR)
Algorithm

Products of Each Microwave
Radiometer Data

Geostationary
Satellites
IR Cloud motion
Kalman filtering

GSMaP Product(IR-MWR Merged Algorithm)
0.1-deg. grid, hourly
32

GSMaP approach - Hybrid approach Combination of the moving vector and GPCP type method
We have decided to combine each method (sampling from both world).

That is…..
1. Propagates the rainy pixels on the moving vector derived from thesuccessive IR images
2. And then, optimally estimates the rain rate from the brightness temperature
at IR wavelength

What is the best way to realize this?
Global precipitation mapping is a sequential process.
So, the Kalman filter is the best way to do this.

GSMaP

Kalman filter method
Refine precipitation rate on Kalman gain after propagating the rain pixel
The Kalman gain is determined from the database on the relationship between
the IR Tb and surface rain rate.

33

Typhoon 200507/BANYAN (hourly)(Blue violet areas show MWR overpasses.)34

Validation of GSMaP products
GSMaP Algorithm details will be provided by Prof.
Ushio (Osaka Univ.)’s lecture, (in addition to Dr.
Aonashi (MRI/JMA)’s lecture)
So, I move on to a topic about validation of the
GSMaP products

35Comparison with the ground–radar (COBRA)
Comparison of TMI retrievals(GSMaP_TMI) with COBRA data for
four selected overpasses during
June 2004 (0.25 x 0.25 deg.)

NICT Okinawa Bistatic
Polarimetric Radar(COBRA)
C-band(5340 MHz)
10 minute cycle

A field campaign of observing precipitation in
Okinawa, Japan during rainy season of 2004
(okn-baiu04)(Kubota et al. 2007)

Correlation : 0.82
RMSE(mm/hr) : 1.37
36

Validation using JMA Radar-AMeDAS analyssis
GSMaP_NRT(26 July 2009)

Radar-AMeDAS(White: missing values)

Comparisons in daily averaged rainfall estimates around Japan with 0.25 x0.25 deg. resolution with reference to the gauge-calibrated ground radardataset (JMA Radar-AMeDAS precipitation analysis).
37

Example : Heavy rain in 14 Jul 2010
http://www-ipwg.kugi.kyoto-u.ac.jp/IPWG/sat_val_Japan.html

Rad
AMeDASar

GSMaP_NRTCMORPH

MWCOMB

3B42RT

3B41RT

3B40RT

7/14

38

Validation results around Japan during 2004
Monthly averaged time series ofcorrelation coefficients with daily
estimates around Japan during
2004.
Horizontal resolution:
0.25x 0.25 latitude/longitude.
Reference: JMA RadarAMeDAS precipitation analysis

Better during the warm season,
related to false rainfall signals over surface snow,
consistent with validation results in other regions (e.g.,
Ebert et al. 2007)

6 satellite products:
GSMaP
TMPA 3B42RT and 3B42 (Huffman et al.
2007)
CMORPH (Joyce et al. 2004)
PERSIANN(Sorooshian et al. 2000, Hsu
et al.1997)
NRL-Blended (Turk and Miller 2005,
Turk and Mehta 2007)

Kubota et al. (2009, JMSJ)
39

http://www.bom.gov.au/bmrc/SatRainVal/validationintercomparison.html

Collaboration in IPWG
The GSMaP joins the International Precipitation Working Group(IPWG) validation activities.
• We validates various satellite estimates around Japan.
• Our GSMaP products are validated in U.S.(J. Janowiak), Australia(E. Ebert ), South America (D. Vila), and Japan every day.

40Comparison of GSMaP and Rain-gauge
1-minute accumulation gauge data in
Bangladesh (provided by Prof. Terao’sgroup) is used in comparison.
Possible problem in differences
between 0.1degree averages (GSMaP)and station data.

Peaks of rainfall by GSMaP are wellcorrespond to that of rain-gauge formost of cases, while GSMaP shows
lower rainfall rates compared to raingauge.
Short-lived rainfall events, in which rain
areas are not detected by the
microwave observation, may not be
detected by GSMaP algorithm.

Rainfall by GSMaP_MVK (13-15 July2005). Dhaka is denoted by △.

Hourly rainfall series of rain gauges in
Dhaka (red) and GSMaP_MVK (black).
41

Advantages of GSMaP Products
Advantages of GSMaP are;faster availability within 4-hours; andglobally homogeneous rainfall distribution in hourly and 0.1
degree resolution (about 10-km resolution).

GSMaP will be effectively used for;rainfall over the ocean where no ground observation isavailable;
rainfall events in large temporal scale; andrainfall events in large horizontal scale.

42

Disadvantages of GSMaP Products
GSMaP will NOT be useful for;rainfall events in smaller horizontal scale than resolution of microwave
observation (a few ten kilometers),
rainfall events in shorter time scale than microwave observation
frequency (several hours); andflash flood whose lead time is less than satellite datatransmission/processing time (4-hours in case of GSMaP near-realtime version).
rainfall/snow events over surface snow

GSMaP tends to show lower rain rates over the land
compared to gauge observation, while rainfall peaks are well
captured.
There are some works to calibrate GSMaP to ground observation inorder to apply to operational use.
43

Multi-satellite product:
Application

44

Rainfall observations by the GSMaPGlobal rainfall image with cloud images on 0UTC, 3rd May 2008

Movie from 28th April to 3rd May 2008
for cyclone “Nargis”

Movie from 11th to 16th November 2007 forcyclone “Sidr”

45

Accumulated rainfall amount
Accumulated rainfall amount, calculatedby the GSMaP_NRT (mm)a) 27April and 3 May 2008
for the cyclone “Nargis”b) 10 and 16, November
2007 for the cyclone “Sidr”

46

Movie of daily rainfall for Nargis

Black circles: Center positions of the cyclone
Period: 27th April 2008 – 3rd May 200847

Daily rainfall amount: 2nd May 2008

Black circles: Center positions of the cyclone
Date : 2nd May 200848

Accumulated rainfall amountRainfall amount accumulated from 27th April 200849

Rainfall amount during 8days

Rainfall amount accumulated from 27th April 2008 to 4th
May 2008. Black circles: Center positions of the cyclone.
50

2011 Thailand floods
Severe flooding occurred during the 2011 rainy season
in Thailand, and caused serious economic damages
and losses due to flooding.

(Photos: Flooding at AIT)51

Oct. 2011 around SE Asia

52

Acculated rainfall amount using GSMaP_NRT
Jun-Sep 2011
Vientian(18N, 102.6E)
Chiang Mai(18.8N, 99E)

Bangkok(13.75N, 100.5E)
Phnom Penh
(11.55N,104.9E)

53

Rainfall variability of several cities
Rainfall variability during March-October by the GSMaP_NRT (15-day-running mean)
Bangkok

Chiang Mai

Vientian

Phnom Penh

54

Comparison to rain amount in 2010
Acculated rainfall amountduring Jun-Sep 2010

Ratio of R2011/R2010
during Jun.-Sep.

55

Monitoring of Chao Phraya River basin
Chao Phraya River

Rainfall variability over Chao Phraya River basinby the GSMaP_NRT (15-day-running mean)

Chao Phraya
River basin isdefined as
99-101E,
13.5-19.5N area.

56

Analysis of Chao Phraya River basin (1)Zone definition of Chao
Phraya River basin
Chao Phraya River
A
B
C
D
E

http://upload.wikimedia.org/wikipedia/commons/
3/35/Chaophrayarivermap.png

57

Analysis of Chao Phraya River basin (2)

A

B
C

D
E

58

GSMaP data handling

59

How to Get the GSMaP Online Data
GSMaP_NRT (NRT:Near-real-time) by JAXA/EORC
http://sharaku.eorc.jaxa.jp/GSMaP/index.htm
Period: Oct. 2008-Now
Availability within 4-hours

GSMaP_MVK (post-processing version) by JAXA/EORC
GSMaP Standard version (Version 5.212)
Period: Mar. 2000-Nov. 201060

GSMaP_NRT: Near-Real-time Data
JAXA’s site
Data will be archived about 4 hours after observation.
Ex., Data of 00:00-0:59UTC will be put at about 5 UTC.

FTP
ftp hokusai.eorc.jaxa.jp

When you want to use GSMaP data, please send yourinformation fromhttp://sharaku.eorc.jaxa.jp/GSMaP/index.htm
Binary & Text files
3600 x 1200 pixels
longitude-latitude elements corresponding to a 0.1 x 0.1 degree
grid that covers the global region from 60N to 60S.

61

GSMaP Products in GIS-Format
Collaboration with Sentinel Asia under APRSAF
GSMaP rainfall data can be displayed overlaying other infromation in
GIS format (GeoTIFF) for flood warning through Sentinel Asia web site
developed by JAXA/SAPC.

http://dmss.tksc.jaxa.jp/sentinel/
62

JAXA/EORC GPM site

http://www.eorc.jaxa.jp/GPM/index_e.htm
63Summary
Multi-satellite rainfall Product
Global rainfall map = Products merged from various sensor datafrom satellites, in addition to other resources (e.g, rain gauges)
Global Precipitation Measurement (GPM)
Global Satellite Mapping of Precipitation (GSMaP)
Brief algorithm flow & Validation

GSMaP application
Cyclone “Nargis”
2011 Thailand floods

GSMaP data handling
How to Get the GSMaP Online Data
Data format
64

65

66

Sizes of Cloud/Precipitation Particles
Snow crystals

Typical rain drop and cloud
drop
300μm

300μm

Typical rain drop

Radius=1mm
300μm
300μm

Typical cloud drop

300μm

300μm

300μm

300μm

Radius=0.01mm= 10μm

Discrimination between
rain and cloud drops

Radius=0.1mm= 100μm(drawn based upon Takeda 2005)

300μm
300μm

(Mosimann et al. 1994)67

Microwave bands
13.8 GHz
TRMM/PR(active sensor)
10, 19, 24, 37, 89 GHz
GCOM-W/AMSR2(passive sensor)
Utility of Microwave Band:
Transparency of Clouds(< 100GHz)
Electromagnetic waves at microwave frequencies
penetrate deeply into clouds or go through clouds, andthereby provide information about the cloud interior.
68

Global Precipitation Mapping

Tomoo Ushio
Osaka University, Osaka, Japan

INTRODUCTION

Scientific and Social Significance of the global
precipitation map

Global rain map in daily to hourly scale
Climate change assessment


Improvement in weather forecastsData assimilation in numerical prediction systems

Flood prediction
Water resource management

Monitor variations in rainfall and rain areas
associated with climate changes and global
warming

River, dam, agricultural water, etc.

Other applications


Agriculture, etc.

Precipitation measurement from space?

Remote Sensing from space using electromagnetic waves
Emission, absorption andscatteringby rain drops, snow flakes etc

Infrared radiometer

Cloud particle
Snow and ice
Mixture ofsnow andprecipitation
Melting layer
Precipitation

Sea Surface Wind

Cloud top temp.
Scattering

Microwave radiometer

Precipitaion radar on satellite
Scattering from rain drops

Radiation

0℃Height

Precipitation characteristics observed by
the space borne sensors
(a)

(b)

(c)

(d)

(a) Precipitation radar
Back scattering from rain drops
High accuracy
Narrow swath width(b)Infrared radiometer：
Cloud top information
Not related to surface precipitation rates(c)Microwave radiometer(19GHz)：(d)Microwave radiometer(85GHz):
Directly measures the emission fromprecipitation particularly in low
frequencies

Excellent!!

Global Precipitation Product

TRMM
TMI

Aqua
AMSR-EADEOS-II
AMSR

DMSP
SSM/I

Microwave radiometer algorithm

Product from each sensor
Geo. Satellite
Infrared radiomter
Cloud motion vector

Mix

TRMM/TMI, Aqua/AMSR-E,
ADEOS-II/AMSR, DMSP/SSMI(F13, 14, 15)による1時間のデータ

Composite
product
0.5degree

30 min.
6 hour1 day
1 monthComposite algorithm of
IR and MWR
0.1degree・30min6 hourly MWR combined map
TMI

AMSR & AMSR-E

Combined
6 hourly

SSM/I （F13, F14, F15）

Global Precipitation product

TRMM
TMI

Aqua
AMSR-EADEOS-II
AMSR

DMSP
SSM/I

Microwave radiometer algorithm

Product from each sensor
Geo. Satellite
Infrared radiomter
Cloud motion vector

Mix

TRMM/TMI, Aqua/AMSR-E,
ADEOS-II/AMSR, DMSP/SSMI(F13, 14, 15)による1時間のデータ

Composite
product
0.1degree

1 hour
6 hour1 day
1 monthComposite algorithm of
IR and MWR
0.1degree・30min

How can we get a global precipitation map
with temporal resolution of 3 hours or less?


Infrared radiometers (IR) can provide information on cloud top layers (not precipitation)
 Can ensure a global coverage with high temporal resolution (> 30 min)due to the geo-synchronous orbit (GEO)

Microwave radiometers (MW) Can detect cloud structure and precipitation with high spatial resolution
 The major draw back is temporal sampling due low earth orbit satellite

(LEO)



The LEO-MW and GEO-IR radiometry are quite complementary formonitoring the highly variable parameters like precipitation.

Global High Time/Space Resolution Precipitation Map
 CMORPH (CPC Morphing) – NOAA PERSIANN (Precipitation Estimation from RemotelySensed Information using Artificial Neural Networks )- University of California Irvine TRMM 3B42 – GSFC, NASA
 TRMM 3B42rt – GSFC, NASA NRL Blended - Naval Research Laboratory
 NESDIS Hydro Estimator (STAR) - NOAA Eumetsat Multi-Sensor Precipitation Estimates(MPE)GLOBAL SATELLITE MAPPINGOF PRECIPITATION (GSMAP)Four pillars of the research

GEO
meteorologicalsatellite

Production of global precipitation maps

TRMM/TMI

Improvementof algorithm

Aqua/
AMSR-EADEOOS-II/
AMSR

DMSP/
SSM/I

Algorithm development
Observation data

IR dataCloud
motionvectoralgorithm

High temporal
resolution map

Precipitation
map
database

Feedback

Internet

Look-up table

Data
dissemination
Ground radar observation

Estimationofrain rate

COBRA data

Radiative transfer equation
Precipitation physical model development

Local
precipitation
map
TRMM/PR

Globalprecipitation
map byTRMM/PR
Observation by TRMM/PR

Database of
precipitation
structure

Rain type classification
Precipitation profile
DSD
Melting layer
Snow

Precipitation
profileGlobal Precipitation Product

TRMM
TMI

Aqua
AMSR-EADEOS-II
AMSR

DMSP
SSM/I

Microwave radiometer algorithm

Product from each sensor
Geo. Satellite
Infrared radiomter
Cloud motion vector

Mix

TRMM/TMI, Aqua/AMSR-E,
ADEOS-II/AMSR, DMSP/SSMI(F13, 14, 15)による1時間のデータ

Composite
product
0.5degree

30 min.
6 hour1 day
1 monthComposite algorithm of
IR and MWR
0.1degree・30minBasis of Rain Rate Retrieval by Microwave Radiometers

Radiation Transfer
Equation
Observation
Data
Verification

Algorithm

Rain rate
Retrieval

Precipitation Physical
Model
Look-up
Table

Rain rate
Rain type, Rain profile
Rain drop size distribution
Melting layer, Snow, e.t.c.

• Satellites observe the brightness temperature, integration of radiation andscattering power.

• The relation between rain rate and brightness temperature is tabulated byassuming precipitation physical model and calculating the radiativetransfer equation. The rain rates giving the nearest brightness
temperature values to the observed ones are considered to be the most
16appropriate estimation.

Flow Diagram of GSMaP MWR Algorithm
Satellite Data
• Brightness temperatures (e.g. 9 channels)• Latitude/longitude information
JMA Objective Analysis Data
LatitudeTemperature Profile
Brightness
GANAL
Longitude(0ºC height)
temperature
Land or Ocean
SSTFirst guess of rain rate (85GHz-Only)

Ocean

Rain / No rain

Land

Correction of LUT using estimated inhomogeneity
Estimation of rain
rate (all frequencies)

Iteration

Estimation by
Scattering Algorithm(37,85GHz)

Output : Surface rain rate

Look-up Table (LUT)
Radiative transfer equation
Brightness temperaturevsRain rate(for every frequency)
Precipitation Physical Model
Precipitation Profile
Bright-band Model
DSDApproach of the GSMaP project We use the Aonashi Algorithm to retrieve rainfall

rate.
 The sensors for the analysis are TMI, AMSR-E,
AMSR, SSMI (F13, 14, 15).
Name

Data available

TRMM (TMI)

Jan. 1998 to Dec. 2005

Aqua (AMSR-E)

Jan. 2003 to Oct. 2005

ADEOS-II (AMSR)

Apr. 2003 to Oct. 2003

DMSP (SSMI:F13, F14, F15)

Sep. 2003, July. 2005 and several

Monthly precipitation accumulation from
DMSP/SSMI (F13, 14, 15) for Sep. 2003

F13

F15

F14

Integrated 6-hour microwave radiometer
precipitation map (GSMaP_MWR)
MWR(TMI+AMSR+AMSR-E+F13, 14, 15 SSM/I)

23

Comparison of monthly rain rates by groundbased rain gauge (GPCC) with GSMaP_MWR
 GPCC Monthly Precipitation(Monitoring) Product （ Rudolf
et al. 2005）

 ground-based rain gauge
 about 7000 rain gauges in theworld 1.0ﾟ ×1.0ﾟ monthly average Analysis method• Tropical Area （20S～20N）• There are at least 2 rain
gauges in the 1.0ﾟ ×1.0ﾟ
• 2003-2005

Regression line : y=1.20 x + 30.8 (mm/month) Correlation coefficient : 0.79
24

If we need a global precipitation
map with higher resolution, howshould we do?
 Solution
 ????

Global Precipitation product

TRMM
TMI

Aqua
AMSR-EADEOS-II
AMSR

DMSP
SSM/I

Microwave radiometer algorithm

Product from each sensor
Geo. Satellite
Infrared radiomter
Cloud motion vector

Mix

TRMM/TMI, Aqua/AMSR-E,
ADEOS-II/AMSR, DMSP/SSMI(F13, 14, 15)による1時間のデータ

Composite
product
0.1degree

1 hour
6 hour1 day
1 monthComposite algorithm of
IR and MWR
0.1degree・30min

Approaches
 There are so many products to realize the

3 hours or less resolution.
 What is the GSMaP approach?

How do we combine the MWR and IR data?
Combination of the moving vector and Kalman filtering methodThe moving vector method was introduced by Joyce et al. [2004].


Joyce R., J. Janowiak, P. Arkin, and P. Xie, CMORPH: A method that produces global precipitation estimates from passive
microwave and ifrared data at high spatial and temporal resolution, J. Hydrometeorology, 487-503, 2004

 Advantage

MWR based approach (not Tb but cloud motion)
 Fast processing time Disadvantage

Not include the developing and decaying process of precipitation

New!!


Kalman filter approach Refine precipitation rate on Kalman gain after propagating the rain pixel The Kalman gain is determined from the database on the relationshipbetween the IR Tb and surface rain rate.

What is the Kalman Filter?
 Example (Simple Gaussian form) Assumption
 All error form Gaussian noise Estimated valueN xe ,  e2

xe ,  e2
 Measurement value

xm ,  m2



N xm ,  m2

What is the Kalman Filter?
 Example (Simple Gaussian form) Optimal variance
1
1
1


 2  e2  m2



N x,  2

 Optimal mean  m2   e2 x 2x  2
x
2  e
2  m
 e   m 
 e   m   e2 x  xe x  xe   22  m
 e   m x  xe  K i

Innovation
Kalman gainState and observation equation
used in Kalman filterxk 1  xk  w （State Equation ）yk  Hxk  v （Observation Equation ）xk ：Rain rate at time kyk ：Infrared Tb
xk 1：Rain rate at time k+1
w ：System noisev ：Observation noise

IR brightness temperature [K]

350

300

250

200

150
0

10

20

Precipitation [mm/h]

30

Kalman Filter
Predicted rain rate

IR Tb

y
Obs. Noise  v

xRefinementxˆ  x  K  y  x 

GSMaP

x̂

Prediction
System Noise

w

 w2
 2
v

x   xˆ
Predicted rain ratexK

 2  4  
2Production of high temporal (1 hr)/high spatial (0.1ﾟ×0.1ﾟ)resolution precipitation map (GSMaP)
Algorithm flow to predict the movement of raining areas by applying the cloud
motion vector of the past 1 hour estimated from the IR cloud image
Infrared (IR) Data
11.4 μ m Geo IR
Present
11.4 μ m Geo IR
1 hour before

1 hr Moving Vector

Microwave Radiometer (MWR) Data
Predicted GSMaP
1 hr MWR
Present

Kalman Filter

GSMaP Data
GSMaP
1 hour before

GSMaP
Present

Backward process

Rain area obtained from the radiometer

Swath of the sensor

Backward process
forward

２hr

３hr

１hr

０hr

Propagating rain pixels along withcloud motion vector every hour

Backward process
forward

２hr

３hr

１hr

０hr

Newly identified rain area

Revisted Path of the microwave sensor

Backward process
forward

２時

１時
０時

３時

Backward process
forward

２時

１時
３時

０時
２時
１時

backward

t+1

t+2

+

+

+

=

=

=

t

Combined global precipitation map-MW radiometer + cloud motion vector with Kalman filter(0.1ﾟ , 1 hour, 8-10 July 2005)
MVK: MWR(TMI+AMSR+AMSR-E+F13, 14, 15 SSM/I)+IR Cloud Motion Vector +Kalman Filter赤外・マイクロ波放射計 複合アルゴリズムによる
降水量（2005年7月9～10日）

45

赤外・マイクロ波放射計 複合アルゴリズムによる
降水量（台風200507号/BANYAN）

46

Correlation between radar and the GSMaP product asa function of the past microwave satellite overpass
0.6
GSMaP_MV

0.5
Correlation

GSMaP_MVK

0.4
With Kalman filter

0.3
0.2

Without Kalman filter
Moving vector only

0.1
0
1

2

3
4
Time (hour)

5

6

7

VALIDATION OF THE
PRECIPITATION PRODUCTS

Hurricane Katrina – August 2005
TRMM 3B42, CMORPH, NRL, PERSIANN

These products are very impressive, but
how good are they really?
It is important to evaluate these new highresolution precipitation products tounderstand their errors and to identify apath to a consensus product that can be
combined with gauge observations andother appropriate information and used forscientific and social applications

Program for the Evaluation of High Resolution
Precipitation Products (PEHRPP)
A comprehensive hypothesis-based collaborative effort tounderstand the capabilities and characteristics of these new
HRPP (High Resolution Precipitation Products)
 Hypotheses: HRPP errors can be characterized by comparing them toindependent observations from rain gauges and radars.
 Errors of and differences between HRPP are meaningful, in that they
can be systematically related to precipitation characteristics and/or
algorithm methodology.
 Improved HRPP can be derived by combining products or methods
based on the observed errors and differences.
 HRPP spatial and temporal variability is realistic on scales
appropriate for scientific studies (e.g., hydrology).
 Numerical weather prediction forecasts of precipitation can be used
to improve HRPP in some locations and times (e.g., high latitudes).


Sponsored by the International Precipitation Working Group(Working Group of CGMS) with broad voluntary participation

PEHRPP Consists of 4 Suites of Activities
Suite 1: Continental/Regional Comparisons

Large areas, long (continuous) time periods
Daily/0.25°x0.25° areas

Current/Proposed PEHRPP Suite 1 Validation Sites

Original:
Australian continent (Ebert), CONUS (Janowiak), UK/Europe (Kidd)

New:South America (Vila), S Africa (Pegram), Korea (Sohn), Taiwan (Jou),
Japan (Ushio), Ethiopia/Sub-Saharan Africa (Dinko), Guangdong(Liang)…

Validation
GSMaP_MVK

CMORPH(NOAA/CPC)

GSMaP_MWR

3B42RT(NASA)Example of validation of GSMaP_MVK
using Radar-AMeDAS (8 July 2005)

GSMaP_MVK

55

Radar-AMeDAS

Evaluation of various high resolution precipitation map
using Radar-AMeDAS rain map
Daily variation of correlation coefficient (0.25ﾟ×0.25ﾟ）) July, 2005
Correlation

1

GSMaP_MVK
GSMaP_MVK+

0.9

Correlation

CMORPH
0.8

3B42RT

0.7

CMORPH: NOAA/CPC
3B42RT: NASA/GSFC

0.6

0.5
7/2

7/3

7/4

7/5

7/6

7/7

7/8

7/9

7/10

7/11

Date

GSMaP_MVK shows high correlation with Radar-AMeDAS throughout the period.
GSMaP_MVK+, produced by adding NOAA AMSU rain rates to GSMaP_MVK, shows particularlyhigh correlation.

GPCP
Daily
(NASA/GSFC)

CMAP(NOAA/
CPC)

降水マップの比較(8-12 Sept. 2003)（左上）：CMAP, 2.5°格子（右上）：GPCP Daily, 1.0°格子
雨量計と衛星推定雨量（IR, SSM/I, TOVS）複合（左下）：GSMaP, 0.1°格子
衛星推定雨量(TMI, AMSR-E, SSM/I)
のIR補間

GSMaP

REAL TIME SYSTEM FOR
APPLICATION

Construction of System for Near-Real-Time
Global Rainfall Maps by GSMaP algorithms

JAXA/EORC has started to release global rainfall data in near real time(about four hours after observations) on the Internet using GSMaP
algorithms.
Look-up Tables for MWR
Production of Global Rainfall Map (hourly)

retrievals (once a day)X

X + 3 hours

X + 4 hours

Microwave Radiometer data
Atmospheric varibales

TMI, AMSR-E, SSM/I(F13,14,15)

GEO IR data

JMA Global analysis (GANAL)JMA MGDSST

Merged data by JWA from MTSAT,METEOSAT,GOES

ftp
Objective
analysis
SST

ftp
RTM
Calculation

Kalman filter noise table(computed byonce a week)

LUT for
MWR

MWR
IR

decoding

Rainfallretrieval fromeach MWR
sensorBlended
MWR-IR
algorithm

Globalrainfallmaps

PublicServer

GSMaP NRT System in JAXA/EORC
Global Rainfall Map in Near Real Time by
JAXA/EORC
http://sharaku.eorc.jaxa.jp/GSMaP/Six hourly animation

Accumulated rainfall amountfrom Apr. 27 to May 5

ReferenceUshio, T., K. Sasashige, T. Kubota, S. Shige, K. Okamoto, K.
Aonashi, T. Inoue, N. Takahashi, T. Iguchi, M. Kachi, R. Oki, T.
Morimoto, and Z-I. Kawasaki, A Kalman Filter Approach to the
Global Satellite Mapping of Precipitation (GSMaP) from Combined
Passive Microwave and Infrared Radiometric Data, J. Meteor. Soc.
Japan, Vol. 87A, 2009

Summary Needs for IR and MWR integration method

are described.
 Some of the high resolution products are

introduced.
 GSMaP_MVK product was introduced.

Precipitation measurement in global warming era
Kenji Nakamura
Hydrospheric Atmospheric Research Center
Nagoya University

International Hydrological Programme
Precipitation Measurement from Space and its Applications
The Twenty-second IHP Training Course
18 November - 1 December, 2012

Mars

Venus

Eclipse by Earth.
Thin atmosphere（http://www.jaxa.jp)

Aurola at 100-150km height. Green light is from atomic
oxygen.

H
Water molecule

He

Electric diple
Hydrogen bond
O

C
N

Si Mg

Ne Fe

S

High melting, boilingtemperature
Large heat capacity
High capability to solve
materials
Ice is lighter than wateretc.

Number density of elements in solar system relative to 1 M hydrogenatoms.

（from NASA website）

（from NASA website）

OZONE HOLE

Monreal protocol

http://photozou.jp/photo/

Long range transport of dust, aerosol

Arctic Ice
distribution(from JAXA website）

Spectra of incoming short waves and outgoing long waves (Wikipedia ）Energy budget of Earth surface （IPCC AR3）

Increase of
CO2

（JMA、
http://ds.data.jma.go.jp/ghg/kanshi/ghgp/21co
2.html）

度C

With CO2 increase

El Chichon：1982
Pinatubo：1991

度C

Is

Greenhouse effect
Tg = 2^(1/4) Ta ～ 1.2
Ta

Is=σTa^4

大気

2×Is=σTg^4

Is=σTa^4

地面

Atmospheric temperature profile inradiative-convective equiliburium state（Manabe and Stricker, 1964)

Demonstration of ameteorological observation
balloon with helium.
The balloon inflates in upper
air, and the temperature
lowers.
Water vapor condensates,and produces clouds andprecipitation.

http://www.minumatanbosaitama.jp/contest.htm

http://image.search.yahoo.co.jp

http://www.publicdomainpictures.net/

Climate category (Wikipedia)

Month and pentad
beginning 1979; 2.5°
global coverage.

Mean annual cycle (above) and global mean precipitation (below)

“CLIMATE CHANGE 2007”, (CAMBRIDGE UNIVERSITY PRESS), Figure 3.12.
254

“CLIMATE CHANGE 2007”, (CAMBRIDGE UNIVERSITY PRESS), Figure 3.14.
257

“CLIMATE CHANGE 2007”, (CAMBRIDGE UNIVERSITY PRESS), FAQ 3.2, Figure 1.
263

Tropical Rainfall Measuring Mission: TRMM
Solar paddle

High-gain antenna
TMI

VIRS
LIS

CERES

PR

Observation of tropicalrainfall (Driving engine ofglobal atmosphere)
US-Japan joint mission(Japan: PR, Launch,
US: Bus, 4 sensors,operation)
Launched in Nov., 1997.
Still under operation

Orbit
Altitude
Inclination

Circular（Non-Sun Synchronous）
350km (402.5km since Aug. 2001) (±1.25km)
35 deg.

SensorPrecipitation Radar (PR)TRMM Microwave Imager (TMI)
Visible and Infrared Scanner (VIRS)
Clouds and the Earth’s Radiation Energy System (CERES)

Lightning (LIS)First space-borne
precipitation radardeveloped by CRL and
NASDA
COMMUNICATIONSRESEARCHLABORATORYGlobal Rain Distribution(JAXA wetsite)Global Monthly Accumulated Rain by TRMM/PR(Estimated Surface Rain 1997/12 – 2010/05)

350 km402 kmA-side

The total decrease in PR e_surface rain by altitude change is estimated to be
5.90% on average in a global scale.
Data continuity was kept by calibration for B-side H/W.

Bside

(Adler et al., 2008)

(Adler et al., 2007)

Terrain of the analysis region

Examples of regional precipitation system characteristicsmRainfall [mm/day]
Rainfall DistributionAM

JJA

Comparison of GSMaP and TRMM-PRAM

JJA

Storm height
GSMaP_MWR / PR
mm/day1998-2006, 0.25°× 0.25°

Vertical profile of rainfall• AM– Strong intensity up tohigh altitude– Decrease intensity at thenear surface• JJA– Weak rain at high altitude– Continues to increase
from top to the nearsurface.

Vertical profile of area-averaged (8991E, 23-25N) conditional rain rate.

AM

JJA

Atmospheric fields (AM)Dry air(from Inland of India)

N

Middle: dry

Deepconvection
Convective
instability(high)
Orographic lifting

Low: wet

Convections are
initiated.
Meghalaya Plateau

Moist air(from the Bay of Bengal)

Active：Strong wind at low-level
Break: Convections aresuppressed by mid-level dry air.

Atmospheric fields (JJA)
Active: Significant moisture
convergence over Bangladesh
Break: Moisture flow can easily
escape into the Ganges plain.

Flow out of moisture flux
(to the Ganges plain)

Middle: wet
Moist air(from the Arabiansea)

Rainfall with low rain top height

Low: wet

Convective
instability(low)
Moisture
convergence
Meghalaya Plateau

N

Moist air(from the Bay of Bengal)

Topography
One-step topography

Two-step topography (CHR)

stu
dy
are
a

(m)

Central Himalaya Region (CHR): Three sub-parallel ranges
First topographic barrier
Sub Himalayas (Siwalik Range) (400-1,200 m MSL)
Major topographic barrier
Lesser Himalayas (1,200-3,000 m MSL)
Great Himalayas (> 3,000 m MSL)Bookhagen and Burbank (2006)

PurposeTo explore the impact of two-step topography in the central
Himalayas and the associated rainfall processes.

Horizontal distribution of rainfall

Lat.

Contours: 500, 2000 and 4000 m AMSL
MAM

Relatively larger amount of rainfall
over higher altitude in MAM.

JJA

Clear double band of rainfall
maxima only in JJA over CHR.

Lon.

Rain characteristics in JJA
Rain conditioned rain rate: Near-surface rain rate greater than 0 mm/h.
Rain frequency: Number of rain sample normalized by total number of sample.
Strong rainfall over
low-altitude areas.

Rain cond.
RR
JJA
Frequent rainfall overhigher altitude areas.

Frequency

Rain Characteristics in JJAArea averaged analysis:- Rainfall characteristics are averaged for each 200 m
altitude interval up to 5,000 m MSL.

• Double rainfall peaks• Primary peak (~500 m AMSL): Rainconditioned rain rate is dominant.
• Secondary peak (~2,100 m AMSL):
Frequency is dominant.

 Storm height is approximately 6 km
AMSL, with clear peak near the top of theslope and a small peak at the bottom.

Dry and wet periods
Definition: Wet (dry) phase when daily
rainfall over Himalayan foothills (82 0-900E
and 260-27.50N) is more (less) than 0.5
standard deviation from the average of JulyAugust for each year for a minimum of threeconsecutive days (Singh and Nakamura,2010).

No. of wet days: 83
No. of dry days: 144

Wet

Identification of wet and dry periods
2007
JA meanDry days

Wet days+/-0.5σAverage wet days: 7.55
Average dry days: 13.09

Dry

Rain type
Convective – Stratiform rain rate (mm/hr)

Convective rain

Stratiform – Convective rain events• Intense convective rainfall
over the foothills• High frequency of stratiform
rainfall over higher elevation

Rain characteristics
B

Rain percentage

Rain-cond. rain rate

Rain frequency (%)

Elevation (m)Rain-cond. Rain rate (mm/hr)

A

Rain fraction (%)

Rain total (mm/day)Rain rate

A

B

A

B

Rain frequency

Elevation (m) Lower elevation: Wet phase rainfall dominant Higher elevation: Dry phase rainfall dominant

Active: Shallow systems with intense rain
Break: Shallow systems with weak rainAtmospheric stability (84.5°E)

B

Shaded: Equivalent potential temperature (K)Vector: Horizontal wind (m/s)
Contour: Specific humidity (gm/kg)

A

JJA: atmosphericstability is higher.
MAM: Convective
instability

A

B A

B

A

B A

B

Distributions are
remarkably similar toeach other except forhorizontal wind.

Summary of the precipitation system characteristics
over southern slope of Himalayas

Summary
Bangladesh
Clear pre-monsoon and monsoon season
Difference in atmospheric stability due to wind systems.
Pre-monsoon and monsoon difference
Active and break difference.
Nepal
Strong topography effect causing local differences
Difference in atmospheric stability due to windPre-monsoon and monsoon difference
Active and break difference.

Global Flood Alert System (GFAS) – Streamflow

Applicability and potential of satellitebased data for flood analysis~Experiences of ICHARM through development &application of Integrated Flood Analysis System(IFAS) and Rainfall-Runoff-Inundation (RRI) Model ~Kazuhiko FUKAMI, Seishi NABESAKA,
Takahiro SAYAMA, Mamoru MIYAMOTO
and Ai SUGIURAInternational Centre for Water Hazard and Risk Management
under the auspices of UNESCO (UNESCO-ICHARM),
Public Works Research Institute (PWRI), Japan

Trend of water-related disasters
by type of hazard

Made by ICHARM based on CRED EM-DAT

Increase of vulnerability + climate change?

Flood warning can reduce casualties!
China Floods

Bangladesh Storm Surges Myanmar NargisYear Death Tolls Year

Death Tolls

Year

1931 3 700 000(400 000*)1970

300 000

2008

1954

30 0001991

139 000

1998

3 700

2007

4 200

138 000

Integrated flood risk
management- Flood warning- Hazard and risk analysis
based on “data”
Climate change impact
analysis and adaptation

Problems of flood forecasting system installationin poorly-gauged river basins
Difficulty to get real-time hydrological data in the upstream of atransboundary river basin
Insufficient of implementation and maintenance of ground-based realtime hydrological observation stations, such as raingauge and riverdischarge gauging station with data transmission system.
Lack of the data required for creation of a flood forecasting model suchas altitude, land use, river channel network, etc.
Difficulty of the expense burden which is needed for a flood forecasting
system installation
Insufficient framework to enhance technical capabilities
Rain gauge

Rain gauge
Rain gauge

No transmission
Rain gauge

Rainfall observationby hand

Houses built along ariver

Rain gauge

OfficePossible approaches…
In any case, in-situ hydrologic monitoring is always VERY important.
But it may require very long time to implement any sufficient network
system …
Community-based approach- Simple on-site warning devices, etc.

Empirical & statistical approach using limited hydrologic (rainfall,water level and/or streamflow) data
To start from the utilization of global earth observation data suchas satellite-based rainfall and globally-available GIS data, and to
improve in-situ hydrologic observational network, step by step or
gradually, depending upon the availability of resources
If you have enough resources now, then you can make a fullypackaged system immediately…
4Concept of development IFAS&
Introduction of satellite-based rainfall data
IFAS was developed originally by a joint research (FY2005-2007)among the following:Integrated Flood Analysis System (IFAS)
Flood runoff analysis system with satellite-based rainfall & global GIS information
Rainfall observation
with satellites

Satellite-based rainfall data
（JAXA-GSMaP）(Quasi-real-time)

Rainfall observation
with ground-basedraingauges

GIS information on basincharacteristics(Elevation, Land use, Geology)
Available on internet

Courtesy of JAXA

Integrated Flood Analysis System

User friendly display

Input of ground-based and satellite-based rainfallwith the original self-correction algorithm
Easy creation of distributed-parameter runoff modelwith global/local GIS
Implementation of necessary GIS tools andGUI interfaces on IFAS

Towards
reduction/preventionof flood disasters

Flood forecasting / warning Safe evacuation

Flood runoff ( flow &water level) analysis

<7>Outline of IFAS
Satellite-based rainfall or
Local data (Ground rainfall)

Global GIS datasets
Elevation data, Land use data, etc.

Image Source : JAXAIFAS (Integrated Flood Analysis System)
Data input

Run-off analysis

Model creation

OutputCalculation of river discharge
Runoff Analysis Engine : PWRI Distributed hydrological model
Upper(PDHM Ver.2)
tank
Lower
tank
River
tank

Upper area

Lower

GFAS - Rainfall
http://gfas.internationalfloodnetwork.org/gfas-web/

Heavy rainfall identification  alert

Real-time estimation of rainfall areas
Exceeding 10- (or 5-) Year Return Period

Real-time Rainfall Map (every 3 hour)Pre-analyzed rainfall distribution
exceeding 5 or 10-year return period

Satellite-based rainfall data
There is no necessity for installation and maintenance of a rain gauge
or transmission equipment .
Ground-based rainfall data are indispensable to get highly-accurate flood runoff analysis and forecast.

Almost the worldwide coverage and a consistent accuracy are obtained.
Resolution (time and space) and observation accuracy are low
compared with properly-distributed ground-based rainfall data.
Product name

3B42RT

CMORPH

GSMaP_NRT

Developer and provider

NASA/GSFCNOAA/CPCJAXA/EORC

Coverage

N60°- S60°

Resolution

0.25°

0.25°

0.1°

Resolution time

3 hours

3 hours

1 hourTime lag

10 hours

15 hours

4 hours

Coordinate system

WGS

Historical data

Dec 1997-

Dec 2002-

Sensors

TRMM/TMI
Aqua/AMSR-E
AMSU-B
DMSP/SSM/I
IR

Aqua/AMSR-E
AMSU-B
DMSP/SSM/I
TRMM/TMIIR

Dec. 2007～

GSMaP_nRT

TRMM/TMI
Aqua/AMSR-E
ADEOS-Ⅱ/
AMSR
SSM/I
IR
AMSU-B

http://sharaku.eorc.jaxa.jp/GSMaP/in
dex.htm

Algorithm for self-correction of satellite-based
rainfall data without any ground-based rainfall
Moving fast Underestimation
Moving slowly  Better coincidence
data
Present

Present＋＋

１hour later＋

2 hour later

１hour later＝＝

2 hour later3h additional
rainfall

3h additional
rainfall
Small spatial variance of cumulative rainfall

Large spatial variance of cumulative rainfall

on a certain scale

on a certain scale

150
USA
Japan-Yoshino
GSMaP_MVK+(mm/3h)
GSMaP（mm/3h）

Japan-Tone
Japan-Sendai
100

Relative
error [%]

Raw
data

Selfcorrecteddata

Yoshino R.

84.0

20.2

Sendai R.

47.1

-4.8

Low orographic rainfall
in lower layers of atmosphere
Almost good estimation
with high-frequency microwave
radiometer aboard satellite

Raw GSMaP

0
50

100

ground-gauged rainfall
precipitationdata(GGP)(mm/3h）(mm/3h)Ground-basedHigh cloud
speed

Low wind speed

Improvementofcoincidence0

A hypothesis on the reason why thisself-correction is empirically effective.
Low cloud speed

Self-corrected
GSMaP

50

＋

150

High wind
speed

Heavy orographic rainfall
in lower layers of atmosphere
Underestimation
with high-frequency microwave
radiometer aboard satellite

Default
setting
ICE

Look-up table:
Relationship between microwave brightness
temperatures (Tbs) and rain rates

WATER

LUT@85 GHz
Observed
Brightness
Temperature

Original

Orographic Rainfall

Orographic Rainfall

ICE

Underdstimation

WATER

Retrieved Rainfall
Intensity
From Taniguchi and Shige (2011) and Ashiwaki(2010)

Effect of the ICHARM’s self-correction method ofsatellite-based rainfall
Self-corrected GSMaP_nRT can
effectively reduce the degree ofunderestimation for heavy rainfall data
without any real-time ground-based
rainfall data.
IFAS implements this selfcorrection method.
Ground gauged rainfall

GSMaP(original)

GSMaP(corrected)

Typhoon No.8 in 2009 (Typhoon Morakot)
Rainfall distribution in Taiwan (3-hour cumulative rainfall)

Main features of IFAS:
Not only ground-based but also satellite-based rainfall data area
applicable
Distributed-parameter flood runoff model creation using global GIS data
With limited historical / real-time hydrological databasesin poorly-gauged rivers
All-in-one package for GIS data analyses
Free download for the executable programfrom ICHARM-IFAS website
http://www.icharm.pwri.go.jp/index.html
Prompt and efficient implementation of flood analysis and forecasting
system even in poorly-gauged rivers
andstep-by-step improvement of accuracy
with the enhancement of in-situ hydrological observational

network

Default runoff analysis models on IFAS
Three types of distributed hydrological models
PWRI Distributed Hydrological Model (PDHM Ver.2) (simplified for flood events, below)- Suzuki, Terakawa & Matsuura (1996), Inomata & Fukami (2007), IFAS Ver.1.2 Manual (2009)

PWRI Distributed Hydrological Model (PDHM Ver.1) (for flood & long-term flows)
3-layer model for wide availability from low to high flows- Yoshino, Yoshitani & Horiuchi (1990)

 released in IFAS Ver.1.3

BTOP Model (for a variety of hydrological conditions)- Takeuchi, Hapuarachchi, Zhou, Ishidaira & Magome (2008)

 upon special request

PDHM Ver.2

2-layer modelfor quick flood
runoff simulation

Upper
Tank

Lower
Tank
River
Tank

Upper area

Lower area

<15>

Runoff Analysis Model on IFAS (PDHM Ver.2)
input

Hyetograph

RainfallExpression by tank

RainfallRainfallTime

Infiltration

Evaporation

modeling

Hydrograph

Discharge

Groundwater flowTime

Groundwatertank

RunoffoutputSurface flow
Subsurface
flow

UnconfinedgroundwaterRiver course tank

SurfaceTankrunoffConfinedgroundwater

Surface Tank (Upper tank) Groundwater Tank (Lower tank) River Course Tank
①
AUD

Sf2
HFMXD

Au (h − S g ) 2 A
2

②

HIFD

Sg

Sf1 HFMND

Sf0

HCGD

HIGD

Ag hA

HFOD
AGD

③
5

①Surface flow:1
L (h − S f 2 ) 3 i
N SNF②Subsurface flow:
FALFX③Infiltration:

Af 0 (h − S f 0 ) /( S f 2 − S f 0 )SKF

It is possible to set α
as groundwater loss.

Parameters of upper tankvaries by land cover(forest, bush and meadow,
cropland, urban & water)

RRID

=B

1 53
h
i
n

Even if you have nocross-section dataof river channels, it ispossible to calculate
flood flow in steep river
channels throughthe Manning Formulawith the assumption
or wide rectangularchannels.

Flood runoff simulation model creation using
global GIS data
Import data
Example of elevation data of a each cellType

Elevation

Land use

Geology

Soil type

Product

ProviderGlobal Map(Elevation data)

ISCGM

GTOPO30

USGS

Hydro1k

USGS

GLCC

USGS

Global Map(Land cover)

ISCGM

Global Map(Land use)

ISCGM

Geology

CGWM

Soil Texture

UNEP

Soil Water Holding Capacity

UNEP

Soil Depth

GES

and a river channel network

116.5

116.4

181.8

198.7

114.2

95.6

110.5

114.8

123.0

91.2
→94.2

98.5

87.3

164.0

93.5

93.2

94.5

Modify elevation until all sells are
decided their flow directions

Creation of River channel network and basin shape based on elevation data
Elevation

Flow direction generation

Basin boundary andpseudo river-channel generation(Flow direction is determined ateach cell; then, the basinboundary is generated.)(The topography of red cells is
automatically corrected for flow
direction generation.)

Parameter estimation using GIS data
1. Import GIS data
GIS data
Land use/Land cover

2. Distribute GIS data
into some classes

3. Input value
for each tank

Land use classification Surface(GlobalMap)
parameter

Infiltration Rough○○capacity -nesssurfacegroundwater

4. Set value foreach cell

Broadleaf Evergreen Forest
Broadleaf Deciduous Forest
Needleleaf Evergreen Forest

soil

Needleleaf Deciduous Forest

10.00050.7…

Mixed Forest

Parameter set

Tree Open
Shrub
Herbaceous

geology

Herbaceous with Sparse Tree/Shrub
Sparse vegetation

20.000022…

30.000012…

40.000001 0.1…

50.00001…

Bare area (gravel, rock)
Bare area (sand)Cropland
Paddy field
Cropland / Other Vegetation Mosaic
Mangrove
Wetland

Imported GIS data

Urban
Snow, ice
Water bodies

2

IFAS has already set
default parameter.
Each parameterreflects local condition.

River course model parameter estimation
using Cell type classification
From surface,groundwater and otherriver course model

Upper
tank
Lower
tank

River course flow
RRID

River
tank

Upper area

Lower area

Cell type 0

Cell type 1

Cell type 2

Cell type 3

Number ofupper cell(default)

1～2

3～4

5～64

65～

Constant ofResume law

-

6

7

8

Manningroughnesscoefficient

-

0.07

0.05

0.035

・・・

-

・・・

・・・

・・・

Cell type3 routing by the Kinematic waving method.
(displayed as a main river channel)

B×RBH

=B

1 53
h
i
n

B×RBH

hc = RHB×BRHS
B

RBET

Consideration of flood control regulation withdam operation in IFAS
1) Fixed release-rate
operation

Fixed Rate
Outflow

Control Start Discharge

Max. Outflow

2) Fixed release-value(amount) operation

Control Start Discharge

3) Fixed-rate and fixedvalue releasing
operation

Max Outflow

Control Start Discharge

Fixed Rate Outflow

4) Arbitrary regulationcurve-based operation(up to five storage levels)

Interface display
Main display

Edit display of rainfall data

Setting display of parameter

Calculation result

Calculation (Plane view)

Plane view on Google Map

Cross section

HydrographTank condition

New functions in IFAS ver1.3 prototype
Automatic download function every hour forstoring satellite-based rainfall
Modification of self-correction method for GSMaP
Implementation of 3-tank hydrological model(PDHM Ver.1)
Effect of loss into deep groundwater
Simple real-time calibration method of calculated
discharge
Each time-step data storing function for the stateof each tank
Automatic early warning system (alert window &e-mail)21

New IFAS-extra-module for Automatic Warning System(IFAS Ver.1.3)(automatic incremental simulation for each time step andalert window & e-mail)Potential applications of IFAS
Tool for fundamental GIS & hydrologic data analysis
Delineation of river basin boundary, river channel network, basincharacterization, etc.
Rainfall analysis (temporal and spatial distribution) in poorly-gauged
river basins with satellite-based and ground-based data

Flood hazard/risk assessment & integrated flood
management
Flood runoff analysis for extreme events
Flood hazard/risk identification and hazard mapping
Implementation of flood forecasting / warning system, coupled withnot only ground-based telemetry data but also radar-derived and/or
satellite-based rainfall data

Water resources assessment and management
Long-term hydrologic analysis  drought monitoring / warning

Examples of IFAS applications
to flood runoff analyses / forecasting,their achievements and problems

IFAS-based runoff analysis: Sendai River, Japan
L=137km A=1,600km2
Good accuracy

Discharge (m3/sec)

3,500

Ground-gauged rainfall
Satellite rainfall(3B42RT)

3,000

0

Onobuchi20〃(original GSMaP)〃(corrected GSMaP)
Calculated discharge(Ground)〃(3B42RT)〃(original GSMaP)(corrected GSMaP)〃
Measured discharge

2,500
2,000
1,500
1,000

40

60

Rainfall (mm/hour)

4,000

80

500
0
Parameters are calibrated using
ground-gauged rainfall andmeasured discharge

100
9/6

9/7

9/8
Date (2004,GMT)

9/9

9/10

•A flood-runoff event analysis in the Sendai River basin of Japan was very
accurately reproduced with IFAS using the ICHARM’s self-correctedsatellite-based rainfall data without any in-situ ground-based rainfall data,in spite of the under-estimation of rainfall rate in its original GSMaP
product.

IFAS-based runoff analysis: Chindwin River,
Myanmar
A=27,420km2
Observed

Simulated (2tanks)Simulated (3tanks)

50000

0

45000

25

40000

50

35000

75

30000

100

25000
20000
15000

3-layermodel（PDHM,
Ver.1)

2-layer model(PDHM, Ver.2)

125
150

Observation

Tributary of

Irrawaddy

175

10000

200

5000

225

0
1-May

HkamtiRainfall (mm/day)Discharge (m3/s)

Rainfall (GSMaP-corrected)

Parameters are calibrated using
self-corrected GSMaP and
observed discharge.

250
31-May30-Jun30-Jul29-Aug28-Sep28-Oct•The 2-tank model (PDHM Ver.2) reproduced the 1st major flood peak level and theother major flood peak timings well, but low flows were much underestimated.
•The 3-tank model (PDHM Ver.1) reproduced both major flood peaks (timing andlevel) and their recessions better. The 1st major flood peak level seems
overestimated, but this may show the possibility of inundation in the upstream of
the gauging station.

Flood runoff
analysis in Dungun
River, Malaysia

Nov.23-Dec.31, 2003

Dec.4-28, 2004
Catchment area =2,500km2

Dec.5-18, 2008
Simulated with corrected-GSMaP
Simulated with ground-based rainfall
Observed discharge

UNITEN, DID & ICHARM

IFAS-based runoff analysis: Kikuchi River, Japan
L=71km A=996km2

Discharge (m3/sec)

2,000

Ground-gauged rainfall
Satellite rainfall(3B42RT)

1,500

1,000

underestimated0

Tamana20〃(original GSMaP)〃(corrected GSMaP)
Calculated discharge(Ground)〃(3B42RT)〃(original GSMaP)〃(corrected GSMaP)
Measured discharge

40

60

500

80

0

100

Parameters are calibrated using
ground-gauged rainfall andmeasured discharge

7/4

7/5
7/6
Date (2006,GMT)

Rainfall (mm/hour)

2,500

7/7

Why was the self-correction of GSMaP unsuccessful for this case?

Difference of frequency of Microwave (MWR) observation3500

unsuccessful case : Kikuchi river0

MWR observation

10
Ground gauged rainfall
2500

Discharge [m3/s]

GSMaP
2000
1500
1000

1400

MWR observation

Corrected GSMaP
Calculated discharge(GSMaP)
Calculated discharge(Corrected GSMaP)
Observed discharge

Ground gauged rainfall

20
1200

GSMaP

40
50
60

1000
800
600

10
20
30

30

Rainfall [mm/h]
3Discharge [m /s]

3000

0

1600

Corrected GSMaP
40
Calculated discharge(GSMaP)
Calculated discharge(Corrected GSMaP)
Observed discharge

50
60

Rainfall [mm/h]

successful case : Sendai river

70

70
400

80

80
500
90

200

90

No MWR
MWR
100
0
0
100
observed
7/4 peak
0:00 7/4 6:00 7/4 12:00 7/4 18:00 7/5 0:00 7/5 6:00 7/5 12:00 7/5 18:00 7/6 0:00
9/6 0:00 9/6 6:00 9/6 12:00 9/6 18:00 9/7 0:00 9/7 6:00 9/7
12:00 9/7 18:00 9/8 0:00 during
Date (UTC)
Date (UTC)
rainfallfrequently

Accuracy of rainfall distribution depends
on the frequency of MWR observations(& accuracy of IR-based motion vectors)
 Image of microwave observation

Image Source : JAXA

Ozawa et al (2010)- MWR obs. is once a few hours on average, but not always
guaranteed.
-During no MWR period, rainfall field is transferred by IRbased motion vector.

Global Precipitation Measurement (GPM)
Current Observation System:
TRMM and other orbital Satellites, and 5 Geostationary Satellites

8 Constellation
Satellites

Core Satellite
Dual Frequency Radar
Multi Frequency Radiometer

Satellites with Micro-wave
Radiometers

Observation of rainfall with moreaccurate and higher resolution

More frequent Observation

Adjustment of data fromconstellation satellites

Cooperation ：JAXA (Japan)

NOAA(US),NASA(US),ESA(EU),
China, Korea and others

Dual frequency Radar, Rocket

NASA(US)
Satellite Bus, Micro-wave
gauging measurement–Earth heating Phenomena–Study of Climate Change–Improvement offorecasting system

Global Observation
every 3 hours•IWRM•Flood Forecasting•Forecasting of crop
productivity

Flood in Pakistan, 2010
Heavy rain from late July due to monsoon whichbrought inundation wide-area in the Indus riverand affected about 20 million people.
In the KP province, “flash flood” brought most of
the number of deaths in this flood event.
In Peshawar, it rained 274mm/day (over 2 times
as the highest record before)
GSMaP underestimated. → The ICHARM’s selfcorrection-based or Thiessen-polygon-based
corrections were conducted.
Kabul River Basin

Dir
Peshawar

Area: 92,600 km2
Length: 700 km

GSMaP_NRT(total amount: 7/27~31)

IFAS-based runoff analysis: Kabul River,
Pakistan
50,0000Nowshera

Satellite rainfall(3B42RT)

Discharge (m3/sec)

30,00020,000Ground rainfall data
were
2~7 times as many as
GSMaP ones. This
10,000correction of GSMaP was
made on the basis ofeach ratio of Thiessen0
polygon (Ground/GSMaP).
7/27 7/28 7/29 7/30 7/31

8/1 8/2
Date (2010,GMT)

GSMaP (original)

44.5

6.5

Ground-gauged

99.0

40.0

Rate(Ground/GSMaP)

2.22

69.8

19.5

338.0

63.0

4.84

3.24

6.14

48.8

68.2

28.9

333.0

372.0

219.0

6.82

5.45

7.58

2

4

6

Rainfall (mm/hour)〃(original GSMaP)〃(corrected GSMaP)〃(corrected GSMaP by Ground)Calculated discharge(3B42RT)〃(original GSMaP)〃(corrected GSMaP)〃(corrected GSMaP by Ground)
Measured discharge

40,000Calculated by
Default parameter

8

8/3

8/4

Alghouth the runoff simulation
with ICHARM’s self-correction
algorithm without any groundbased rainfall data seemed best,
this does not necessarily mean
the truth. In any case, thisshows the high potential ofsatellite-based runoff
simulation.

Estimated hydrographs inupper
10 & middle Indus riverwith IFAS were on IFI-home.

32

Comparison between satellite-based inundation extent
and inundation simulations with another ICHARM’sRainfall-Runoff-Inundation (RRI) Model for Pakistan
flood, August 2010
Subsurface flow
(Unsaturated, Saturated）

Slope - River
interaction

Runoff-inundation simulation can interpolate
missing satellite-based information
on flood inundation area
caused by flash flood.

Two dimensional(diffusion wave)
Peshawar

Nowshera

http://www.reliefweb.int/rw/fullmaps_sa.nsf/luFullMap/1063BC946E38009685257788006FF75B/$File/map.pdf?OpenElement

Sayama et al. (2011)

Rainfall downscaling & forecastingwith different initial conditions Ushiyama et al. (2011)
Average inN30-39, E66-78

Average in
Kabul river basin

Ensemble of rainfall forecast with WRF

Ensemble inundation forecast with RRI Model
Hydrograph at Kabul

Inundation probability

Frequency distribution by 13 members

The ratio of members which
maximum water depth exceed 1m.

Forecast at July 26(23 00Z -26 00Z)
Input predicted rainfallof 13 members.

Forecast at July 27(24 00Z -27 00Z)
 Ensemble memberswith initial condition
before the beginning of
rainfall gave better
ensemble mean andprobability range.

probability

Forecast at July 28(25 00Z -28 00Z)
 Ensemble memberswith their initialconditions during therainfall period had large
variance.

Ushiyama et al. (2011)

Development of another new-generationhydrology / hydraulics coupled model
for large-scale river basins~ Rainfall-Runoff-Inundation (RRI) Modeland its high potential ~Chao Phraya River Basin

P1

N1
W21

Chao Phraya River BasinArea

160,000km2

Length800 kmSlope

1/50,000 ~ 60,000(Ayutthaya – River Mouth)Dam

Bhumibol : 13.5 Billion m3
Sirikit : 9.5 Billion m3

Sirikit Dam

m
Yo
n
Na

W4A
Pi Y4
ng
Y16

Outline of 2011 Thailand Flooding
N5A
Pa SakWang

Y1C

Bhumibol Dam
P17

Chainat C2
0-10

30-50
50-100100-150
500-1,000
1,000-1,500
1,500-2,000
2,000-2,572

a

20-30

in
Tha Ch

10-20

C13

Nakhon Sawan
y
hra
oP
Cha

Elev. (m)

N67

C35

Ayutthaya
RojanaBangkokRainfallMay - Oct. : 1372 mm (140% of Ave.)
Five typhoons and tropical low
pressure systems

Peak Discharge

4,680 m3/s on Oct. 14, 2011
(5,960 m3/s in 2006)

Inundation Vol.

Approx. 10,000 km2, 10.0 Bill. m3

DamageDeaths and missing : 678
Affected people : 1.35 Million
Damage and loss : 45.7 Bill. USD

Rainfall-Runoff-Inundation Model (1/3)

Subsurface + Surface
1D Diffusion
in River2D Diffusion
on Slope
Vertical Infiltration• Diffusion Wave Approximations• 1D in River• 2D on Slope• Subsurface flow
• Vertical Infiltration with Green Ampt
• Saturated Subsurface + Surface Flow
Sayama, T. et al.: Rainfall-Runoff-Inundation Analysis of Pakistan Flood 2010 at
the Kabul River Basin, Hydrological Sciences Journal, 57(2), pp. 298-312, 2012.

• Rectangular river cross sections• Width, Depths, Levee heights can be
assigned for each river grid-cells• Over-topping and step-down formulae areused to compute the interactions between
water in river and on slope• Water depth and discharge boundary
conditions can be wet at any grid-cell

Rainfall-Runoff-Inundation Model

Subsurface + Surface

Vertical InfiltrationhhInfiltration : Green Ampt Model∂H(h ≤ d )
,
−
kh

∂
x

qx = 
− 1 (h − d )5 / 3 ∂H sgn  ∂H  − k (h − d ) ∂H , (d < h ) ∂x  n∂x∂x
Takasao & Shiiba (1981)

 (φ − θ i )S f 
f = k v 1 +

F



1
∂H
 ∂H 
sgn qx = − h5 / 3n∂x
 ∂x 

Rainfall-Runoff-Inundation Model (2/3)

Typical Distributed R-R Model

2D Diffusion Model --- Slopeof water level controls flow
direction and velocity (RRI)

Land

River

Rainfall-Runoff-Inundation
Predictionof Thailand Flood 2011
(conducted on 2011/10/14)
W. Depth

Simulation Domain : 163,293 km2
Grid Size : 60sec (1776 x 1884 m)
Simulation Period：
2011/07/01 0:00 (UTC) –2011/11/30 0:00 (UTC)
Input Rainfall:2011/07/01 0:00 (UTC) –2011/11/8 0:00 (UTC)
3B42RT (Satellite BasedRainfall)(Every 3hours, Spatial
Resolution: 0.25 deg)2011/11/8 6:00 (UTC) –2011/11/15 12:00 (UTC)JMA- GSM Weekly Weather
Forecasting（Forecasting Lead Time: 8 days,
Update every 12 hours）
2011/11/15 15:00 (UTC) –2011/11/30 0:00 (UTC)(Previous year’s 3B42RT rainfall
in the same period)

Nakhon Sawan

Ayutthaya

1 : July 1
31 : Aug 1
62 : Sep 1
92 : Oct 1
123 : Nov 1
152 : Nov 30

Bangkok
http://www.icharm.pwri.go.jp/news/news_j/111024_thai_flood_j.html

Emergency response-type prediction (Lv1) andPost flooding simulation with more local data (Lv2)

Sayama et al. (2012)

River Discharges
Chao Phraya River Basin

P1(Observation, Lv1, Lv2)

N1
W21

Sirikit Dam

m
Yo
n
Na

W4A
Pi Y4
ng
Y16

N5A
Pa SakWang

Y1C

Bhumibol Dam
P17

Chainat C2
0-10

20-30
30-50
50-100100-150
500-1,000

in
Tha Ch

10-20

C13

Nakhon Sawan
ya
hra
oP
Cha

Elev. (m)

N67

C35

Ayutthaya
RojanaBangkok

1,000-1,500
1,500-2,000
2,000-2,572

Emergency response-type prediction (Lv1) and post flooding simulation with more local data (Lv2)

Sayama et al. (2012)

Peak water levels
29.1 (2.4)
28.5 (5.1)*
27.1 (3.9)

Nakhon Sawan

24.7 (2.6)
22.8 (2.9)
20.1 (2.8)
Chinat15.8 (4.0)
11.9 (1.8)

8.1 (2.6)
Ayutthaya
5.6 (1.0)
4.2 (1.7)
5.2 (1.1)
4.8 (1.1)
3.8 (0.3)

7.6 (1.5 )

3.7 (0.7)
Bangkok

Red: Land, Blue: River Peak water level(Reduced water level by the end of November )Sayama et al. (2012)

Inundation extents
このイメージは、現在表示できません。

F: Lv2 slightly under-estimates inundation extents
 Water levels in river and on floodplain become independent, becomes difficult to simulateSpatial distribution of cumulative rainfall during July and October 2010(a) Satellite-based rainfall for Lv.1 (NASA-3B42RT), (b)Satellite-ground-coupled rainfall for Lv.2
(Yellow plots show the locations of the ground-based rainfall stations used for the modification)

47

Cumulative and daily basin-averaged rainfall
from July to December 201148

Effect of different input information (sensitivity analysis)

Errors in simulated riverdischarges with different typesof input updates from Lv1
Lv 2: All
C: Cross section
D: Dam
E: ET (Evapotranspiration
P: Parameters
R: Rainfall
T: TideOverestimation in Lv1 was primary
due to the ignorance of ET !

Ongoing projects
for practice and operation

UNESCO Project (2 years: 2012-14)

Strategic Strengthening of Flood Warning
and Management Capacity of Pakistan
A. Strategic Augmenting of Flood
Forecasting and Hazard Mapping
Capacity
A-1 Development of Indus IFAS
A2- Floodplain and Hazard Mapping ofLower Indus

Tarbella
Dam

Realtime flow hydrograph from Existing FEWS

1B. Knowledge Platforms for Sharing
Transboundary Data and Community
Flood Risk Information
B1. International Networking for
Sharing of Transboundary Data
B2. Knowledge platform for timely
national, provincial and district level
data sharing
C. Capacity Development for Flood
Forecasting and Hazard Mapping• Master’s Degree training at ICHARM
for PMD, SUPARCO and FFC on flood
UNESCO
forecasting/warning, hazard mappingand integrated flood management• A short training course at ICHARM on
Punjnad
IWRM and integrated flood
management• Training workshops in Pakistan
conducted by UNESCO Islamabad

Example of Implementation Project for flood early warning
system with satellite-based information
ADB-RETA 7276
inIndonesia to implement IFASbased flood forecasting andwarning systemforthe Bengawan Solo River(FY2009-201２)
Jakarta
Solo River Basin

JST-JICA SATREPS Project on
Research and Development for
Reducing Geo-Hazard Damage in
Malaysia caused by Landslide &Flood(FY2011-2015)
Major target river basin for flood: Kelantan
River
Kelantan
River

500km

Flood in Dec.2007

Training Workshop
with BBWS Solo in
March, 2010

Wide-range analysis:
IFAS
High-res. analysis:
GETFLOWS

Dissemination activities
andfinal remarks

ICHARM Website to download IFAS
(IFAS-PDHM Ver.1.2 & 1.3β)
http://www.icharm.pwri.go.jp/index.html

ICHARM will improve the system continuously to make it moreuser-friendly software and contribute to flood mitigation at local
communities.

IFAS Training workshops (2008 – 2011)
Purpose of the training course
To build capacities to undertake hydrological prediction/forecasting in relatively
ungauged basins using satellite-based rainfall.
Program•Remote Sensing of Precipitation from Space (JAXA)•Introduction of river administration in Japan•Introduction of Global Flood Alert System•Operating procedures for IFAS
•Validation method of satellite-based rainfall•Current conditions and problems in each country
International Workshop on Application and Validation of GFAS
2008: Ethiopia, Zambia, Cuba, Argentina, Bangladesh, Guatemala, Nepal(7countries)
2009: India, Indonesia, Viet Nam, Bangladesh, Nepal, Laos (6countries)
IFAS Seminars in overseas (sponsored by ADB, JAXA, UNESCAP, UNESCO-IHP,etc.)
Nepal (2009), Indonesia, Myanmar, Vietnam (2010),
Pakistan, Vietnam, India (2011), Iran, Vietnam and Indonesia(2012)
Training seminars at ICHARM Master Course & JICA short courses

IFAS to enhance local ownership of flood forecasts& in-situ hydrological observation network on the ground
SystemTraining

Global Data

Low accuracy

Local Data

High accuracy

Conclusion (1/2)
The combination of satellite-based rainfall information, global
GIS data and IFAS (Integrated Flood Analysis System), as apractical toolkit for local users, especially in poorly-gauged
river basins to integrate all those global information easily, hasvery high potential to promptly & efficiently implement flood
analysis & forecasting system, in consideration with further
step-by-step improvements in the future.
RRI model has also high potential for large-scale flood andinundation hazard forecasting and mapping in poorly-gauged
river basins, although the RRI model has not been designed
for simple operational use.
Key minimal information can be acquired through satellitebased and global-GIS-based IFAS / RRI simulations even if
the accuracy is not enough from the perspective of the exact
coincidence of hydrograph, water level and/or flooding area.

Conclusion (2/2)
On the other hand, it should be also noted that, without any insitu (ground-truth) data, such integrated information & analysis
cannot be assured, verified nor improved.
It is, therefore, indispensable to couple satellite, global GIS
and possibly numerical meteorological simulation data with insitu (geographical, geophysical and hydrologic) data in orderto improve the quality (accuracy) of the integrated information& analysis and to upgrade the range & depth of application,
which will lead to the establishment of local ownership of flood
forecasting and warning.