KU-OU Symp., Uji Obaku Plaza, Kyoto Univ., Nov. 11, 2009

Introduction
Backgroundkd

Quantitative estimation of rainfall intensity andhydrometeors mixtureusing
i C-bandC b d polarimetricl i t i radardbased on validation by in-Situ campaign
observation synchronized with Video-Sonde

Research Di
Division
ision of Atmospheric and Hydrospheric
H drospheric Disasters
Disaster Prevention Research Institute (DPRI)
Kyoto University,
University Japan(nakakita@hmd.dpri.kyoto-u.ac.jp)
Copyright 2009 by Eiichi Nakakita

Current activities with C-band polarimetric Doppler radar
Campaign Obs.＋Analysis＋Data Assimilation＋Impact Assessment
C b d polarimetricC-bandl i t i DopplerDl radard (COBRA)
Doppler velocity, ZHH, ZDR, KDP, ρHV, LDR

Estimation and validation of DSD
and type of hydrometeors

Developing algorism for QPE
Ground –based
observations of DSD
with
ith some kinds
ki d off
instruments

One of the ppurposesp¾Promotion of introducing the new CC-band polarimetric radars toJapanese operational radar network.

A i ii
Activities

Eiichi NAKAKITA

Campaign direct
observation ofhydrometeors
synchronized withthe video sonde
and the Hyvis•In Japan, main weather radars for operational network has been the Cband radar.
radar.
•The new type polarimetric radar, which can observe KDP , has not been put
into operational use in Japan.
•Small number of C
C--band polarimetric radar in the world

Data assimilation by mesoscale atmospheric model for
QPF¾A synchronized observation by the C
C--band polarimetric radar, COBRA,
with the videovideo-sonde
sonde..
¾Development of a new operational QPE algorithm for polarimetric with
C-band radar byy an improvementpof existingg algorithm.
g¾Classification as mixture of some types of hydrometeors.
1¾QPF with data assimilation
Copyright 2009 by Eiichi Nakakita

Contents• In-Situ campaign observation synchronizedwithith Video-Sonde
Vid S d• A new operational QPE algorithm for C-bandpolarimetricl i t i radard• Classification of co-existing hydrometeors usinga C-bandC b d polarimetricl ii radard• Operational polarimetric radars in near futureby the Ministryi iof Land, Infrastructure,
Transportation and Tourism (MLIT), and one
off an iimportantt t backgroundb kdImpact assessment onwater management
Copyright 2009 by Eiichi Nakakita

3
Copyright 2009 by Eiichi Nakakita

Contents• In-Situ campaign observation synchronizedwithith Video-Sonde
Vid S d ((~ 5 min.)i )• A new operational QPE algorithm for C-bandpolarimetricl i t i radard (~( 10 min.)i )• Classification of co-existing hydrometeors usinga C-bandC b d polarimetricl ii radard (~( 14 min.)i )• Operational polarimetric radars in near futureby the Ministryi iof Land, Infrastructure,
Transportation and Tourism (MLIT), and one
off an iimportantt t backgroundb kd ((~20
20 min.)i )In-Situ campaign observationsynchronizedywith Video-Sonde(2007~2009)
Eiichi NAKAKITA, Kosei YAMAGUCHI,
Hidenobu TAKEHATA, Yasuhiko SUMIDA, (Kyoto University)
K ji SUZUKI (YKenji(Yamaguchi
hi U
University)ii )Katuhiro NAKAGAWA, Seiji KAWAMURA (NICT)
Satoru OISHI (University of Yamanashi)
Kazuhisa TUBOKI, Yukari SHUSSE, Tadayasu OHIGASHI((Nagoyag y University)y)
Tsutomu TAKAHASHI (University of Hawaii)

4
Copyright 2009 by Eiichi Nakakita

C-Band Doppler Polarimetric Radar

Copyright 2009 by Eiichi Nakakita

Ground--based observations
GroundCOBRA = CRL Okinawa Bistatic
polarimetric RAdarplocations of COBRA and theground observation points.

COBRA is operated by the National Institute of information andCommunications Technology (NICT).

•••

C-band polarimetric weather
radar
COBRA’ss purpose is the
COBRA
development of the mesohydrometeorological observation
for the next generation
generation.
Target : Typhoon, Bai-u, meso scale
rainfall system•

Polarization parameters: ZHH, ZVV,ZDR, φDP, KDP, ρHV, LDR

Copyright 2009 by Eiichi Nakakita

Peak power
Pulse width
PRF
Antenna size
Beam width
Radome size
Cross pol. ratio
Antenna gain
Sidelobe
Ant. scan speed
Polarization> 250 kW (Dual Klystron)> 10 kW (Dual TWTA)
0.5 μs, 1.0 μs, 2.0 μs (Klystron)
0.5 – 100 μs (TWTA)
250 Hz - 3000 Hz, PRT 1μｓ step
(staggered PRF)
4.5m φ parabolic
0.91deg
8m φ> 36 dB (Integrated value in a beam)
45 dBi (including radome)< -27 dB (one way)
0.5-10 rpm(PPI), 0.1-3.6 rpm(RHI),
0.1 rpm step
H, V, +45, -45, LC, RC (pulse by pulse)
6

2-D Video Disdrometer （２ＤＶＤ）
2DVD has two line scan cameras which areorthogonally set at different height. Therefore
it can observe not only the size and shape but
also the terminal velocity of precipitation
particles.

Disdrometer
The momentum of raindrop
particle is observed andcorrespond to the size of theparticle Therefore DSD can beparticle.
observed.

•Disdrometers and optical rain gauge at Ogimi•Rain gauge of AMeDAS, which stands for AutomatedMeteorological Data Acquision System, at Yoronjima
Copyright 2009 by Eiichi Nakakita

Campaign Observation in OkinawaVideo-Sonde and Hydrometeor types

Observation Periods
Preliminary IOP：Nov. 15th – 28th , 2007
Main IOP (1): May 28th-June 21st, 2008
Main IOP (2): May 21th-June 21st, 2009



I crystalIcet l

GraupelSnow flake

Collaboration
Kyoto University, University of Yamanashi, Yamaguchi University,
N
NagoyaU
University,iit University
U i
it off Tsukuba,
T k b Utsunomiya
Uti University,
U iit
National Institute of Information and Communications Technology,
Central Research Institute of Electric Power Industry,
Industry University of
Hawaii(more than 30 researchers and students)



R i
Rain

13ｍｍ

Takahashi at el.(2001)

Observation instruments
・Polarimetric Doppler radar (COBRA)
・Video-Sonde, Hyvis
・2-D video distrometer，Impact type disdrometer，Micro rain radar,
Laser drop-sizing gauge，Optical rain gauge, etc.

¾¾¾

Video-sondeVideosonde is the radiosonde with a video camera
camera.
The video-sonde records images of particles larger than 0.5mm diameter.
The video-sonde is launched with the balloon, and can directly observehydrometeors below and in the cloud.

8

9
Copyright 2009 by Eiichi Nakakita

Example of observations

Video Zonde

No.6
9

no data–20
20

GRAUPEL霰

AGGREGATE

雪片

7

–10
10

HEIGHT(km)FROZEN PARTICLE

6
5
4

雹
ICE CRYSTAL

0

氷晶
10

3

TEMPERATURE (℃ )

8

2
1

雨滴

20

RAINDROP

001
0.1

0 4 0.5
0.4
0 5 0.60 6 0.70.80.9
0 70 80 9 1

45

6 7 8 9 10

DIAMETER(mm)1220(JST) DEC.31 2004

Suzuki et al. (2006)

Copyright 2009 by Eiichi Nakakita

Suzuki et al. (2006)

Copyright 2009 by Eiichi Nakakita

Hydrometeor types
Rain

Synchronized Observation Ice crystal, snow

flake, graupel etc.

Video sonde

Rain drop

Ice
crystall

Type, size and electriccharge of hydrometeorshydrometeors,pressure, temperature,humidity, wind0'

RHI scan in
i theth direction
di ti off theth video
id sondedohgimi
COBRA

0'

Snow
fl k
flake

Graupelp

onna

0'27˚30'

128˚00'128˚30

Sonde release & tracking
Radar operation

12

Copyright 2009 by Eiichi Nakakita

13

Copyright 2009 by Eiichi Nakakita

Scenes in the campaign observation

Scanning of Whether Radar

Volumelscan

RHI scanbefore and after
synchronizationyDuring synchronization

14
Copyright 2009 by Eiichi Nakakita

Copyright 2009 by Eiichi Nakakita

Contents• In-Situ campaign observation synchronizedwithith Video-Sonde
Vid S d ((~ 5 min.)i )• A new operational QPE algorithm for C-bandpolarimetricl i t i radard (~( 10 min.)i )• Classification of co-existing hydrometeors usinga C-bandC b d polarimetricl ii radard (~( 14 min.)i )• Operational polarimetric radars in near futureby the Ministryi iof Land, Infrastructure,
Transportation and Tourism (MLIT), and one
off an iimportantt t backgroundb kd ((~20
20 min.)i )A new operational QPE algorithm
for C
C-bandband polarimetric radar (2008)

Eiichi NAKAKITADisaster Prevention Research Institute, Kyoto University

Hidenobu TAKEHATA
Graduate School of Engineering, Kyoto University

Katsuhiro NAKAGAWA
National Institute of Information and Communications Technology
16
Copyright 2009 by Eiichi Nakakita

In short ……

Copyright 2009 by Eiichi Nakakita

Data set (Ground)¾Mass –weighted diameter Dm will be introduced into estimators
R(Z( HH) and R(K( DP), as R(Z( HH, Dm) and R(K( DP, Dm)).

¾We used the DSD data observedby 2DVD and disdrometers ofi
impacttype at Ogimi
O i i andd Onna.
O¾Ogimi is located 15km and Onnais located 23km from COBRA.

●NiCT●NiCT Site
Site
●AMeDAS●AMeDAS

Ogimi
2DVD & impact type disdrometer¾Dm will be used also in the selection of R(ZHH, Dm) or R(KDP, Dm).

Onna

From 2008

2DVD & impact type disdrometer
AMeDAS=AutomatedAM
DAS A tt d MeteorologicalM tl i l Data
D tAcquisition System
18
Copyright 2009 by Eiichi Nakakita

Copyright 2009 by Eiichi Nakakita

19Strategy of developing the new algorism with Dm

Data set
No. Start Time (UTC) End Time (UTC)

Radar

1

2006. 5.30 23:00 2006. 5.31 11:00

2

2006. 6. 1 15:00 2006. 6. 2

4:00

3

2006. 6. 4

8:00

4

2006 66.10
2006.
10 0:00 2006.
2006 66.10
10 13:00

5

2006. 6.11 6:00 2006. 6.11 12:00

6

2006. 7. 8 15:00 2006. 7. 9 10:00

7

2006. 8. 5 10:00 2006. 8. 6 12:00•COBRA
ZHH, ZVV, ZDR, φDP, ρhv, LDR,
LDR
•Time step every 6 minutes•14PPI(0.5，1.1，1.8，2.5，3.3，4.2，5.3，6.5，
8.1，10.0，12.3，14.8，17.4，20.5°elevation
angle )μ (300m)()•Pulse width
2μs•Beam width
0.9◦

82006. 8. 992006.12. 7 2:00 2006.12. 7 9:00

10

2007. 5.25 18:00 2007. 5.25 24:00

8:00 2006. 6. 5

0:00 2006. 8. 9

2007 66. 5
2007.

4:00 2007.
2007 66. 5 15:00

12

2007. 6. 7

0:00 2007. 6. 7 13:00

13

2007. 6.11 12:00 2007. 6.12 22:00

14

2007. 6.16 8:00 2007. 6.16 23:00

15

2007. 6.18 20:00 2007. 6.19 5:00

Radar data analysisy

DSD data

Radar dataGamma type DSD model：μPreliminary procedure
Estimated
Polarimetric pparameters

5:00

11

DSD data analysisyGround data
•Impact type disdrometer (2006-2007)•2DVD(4-11 June. 2006)•AMeDAS(2006-2007)

Optimized estimator

Polarimetric parameters

R(ZHH),R(KDP)
Existing algorithm of rainfall estimation

Mass weighted diameter (Dｍ)
optimized equation

AMeDAS=Automated Meteorological Data
Acquisition System

Dm(ZHH，ZDR)
New algorithm

20
Copyright 2009 by Eiichi Nakakita

Preliminary procedure (1)21Copyright 2009 by Eiichi Nakakita

Preliminary procedure (2)• Removing of the radar echoes by nonmeteorological factor(such as ground clutter) using ρHV (ρHV<0.9)
<0 9)

ZHH

ZDR

KDP

• Calibration of the system offset of ZDR using 2DVD and
impact type disdrometers measurements.
• Attenuation correction with KDP using the self-consistentmethod byy Bringi
g et al ((2001))• Finally,y, the radar data was converted into 100m mesh data，
and the mesh data was spatially averaged over 1km ×
1km mesh．
22
Copyright 2009 by Eiichi Nakakita

23
Copyright 2009 by Eiichi Nakakita

0 650R( Z HH ) = 3.94
3 94 × 10−2 Z HH 0.650

Estimation of mass-weighted diameter (Dm)

0 813
R ( K DP ) = 30.56
30 56 K DP 0.813•This fig. shows the comparison between
the observations Dm and the estimations Dm
by radar observations
observations.
•Time series of radar-estimated Dm (blueli ) wellline)
ll explainl i thatth t off observedbd Dm (bar).
(b )Dm(ZHH
H, ZDR) (radar)

Optimization of basic estimators using observed DSD

4.0
3.5
30
3.0
2.5
2.0
1.5
1.0
05
0.5

•Data: impact type disdrometer at Okinawa
p (2006-2007)()
in Japan•DSD is Gamma distribution•Axis ratio model of raindrop•Type of basic estimators:
R( Z HH ) = a0 Z HH b0 , R ( K DP ) = a1 K DP b1
Z HHg = ∫Copyright 2009 by Eiichi Nakakita

D 6 N ( D)dD

Z DRg = 10 log10r ( D) = 1.0048 + 5.7 × 10−4 D − 2.628 × 10 −2 D 2+ 3.682 × 10−3 D 3 − 1.677 × 10−4 D 4

Dmax

0

∫
∫

0

K DPg =

Dmax

0

Dmax

D 6 N ( D)dDr ( D)7 / 3 D 6 N ( D)dD

R ( Z HH ) = 3.59 × 10−2 Z HH 0.634

1.5

2.0

2.5

3.0

Dm (disdrometer)

3.5

4.0

Dmax
3
⎛
∫ r ( D) D N ( D)dD ⎞⎟10−3 CkWg ⎜ 1 − 0 Dmax
⎜
⎟
3
λ
⎜
∫0 D N ( D)dD ⎟⎠
⎝

180

25
Copyright 2009 by Eiichi Nakakita

Optimization of estimators depending on class of Dm (1)R(ZHH)

0.5 1.0

10 JJune, 2006
Time series of radar-estimated Dm.

Dm [mm]A
AssumptioniDm = 0.54 Z HH 0.1112 100.917(0.1ZDR )Optimization of estimators depending on class of Dm (2)

R(KDP)

R(ZHH, Dm)

R( K DP ) = 30.56 K DP 0.814

R(ZHH) .VS. Rg and R(KDP) .VS. Rg depend on the size of Dm.
These relations can be further sophisticated using
Dm. by Eiichi Nakakita
Copyright 2009
26

⎧3.49
3 49 × 10−2 Z HH 0.724
⎪
0.852
−3
⎪7.72 × 10 Z HH⎪
R ( Z HH , Dm ) = ⎨3.39 × 10−3 Z HH 0.878
⎪
−3
0.867
⎪3.13 × 10 Z HH
⎪1.60 × 10−4 Z 1.083
HH⎩

R(KDP , Dm)

00.0
0 ≤ Dm < 11.00
1.0 ≤ Dm < 1.5
1.5 ≤ Dm < 2.0
2.0 ≤ Dm < 2.5
2.5 ≤ Dm < 3.0⎧45.62 K DP 0.776 0.0 ≤ Dm < 1.0⎪0.903
1.0 ≤ Dm < 1.5⎪44.63K DP⎪
0.911
R ( K DP , Dm ) = ⎨35.00 K DP
1.5 ≤ Dm < 2.0
⎪
0.935
29.52
K
2.0 ≤ Dm < 2.5
DP⎪⎪22.57 K 1.031 2.5 ≤ D < 3.0
27DP⎩
Copyright
2009 bymEiichi Nakakita

Developed algorithm for switching between R(ZDR) and R(KDP)• Basic algorithm⎧⎪ R ( K DP )Ralg = ⎨⎪⎩ R ( Z HH )Validation using radar (1) comparison between algorithms
R(ZHH)

if Z HH ≥ Th , z and K DP ≥ Th , k ,R(KDP)

otherwise.
R(ZHH)

Dm = 0.54Z HH 0.1112 100.917(0.1ZDR )Mean Bias [mm] RMSE[mm] CC

Ratio of rain total [%]-1.245
1 245

75 60
75.60

4 42
4.42

0 873
0.873

R(KDP)-1.101

2.91

0.922

94.04

Ralg(ZHH, KDP)

-0.543

3.25

0.901

89.39

Ralg(ZHH,K-0.092 both bias 2.99
0.915coefficient
98.20
DP,Dimprovedm)Introducing
Dm
and correlation!!

• Developed algorithm

Ralg(ZHH, KDP)

⎧ R( K DP , Dm ) if (( Z HH ≥ Th , z or Dm ≥ Th , dm )⎪Ralg = ⎨andd K DP ≥ Th , kdp ) Dm is used also for judging⎪
which should be used,⎩ R( Z HH , Dm ) otherwise.

Dm

R(ZHH, Dm) or R(KDP, Dm)

28
Copyright 2009 by Eiichi Nakakita

Validation(2) comparison with current operational QPE (Radar-AMeDAS)
Radar-AMeDASCOBRA with new alg
alg.
¾Comparisons of 1-h rainfall between COBRA's
estimates usingg proposedp p
the new algorithmgandthe Radar-AMeDAS precipitation data
¾Radar-AMeDAS precipitation data is the data
which is calibrated with ground rain
rain-gauges
gauges andsynthesized using the conventional radars inJapan.

30
Copyright 2009 by Eiichi Nakakita•These figures
gshow comparisonpRalg(Zthe
KDP,D) ) observations
HH,,K
DP,D
mm
betweenraingauge
and radar observations.
•BasicBasic algorithm Ralg
l (ZHH, KDP) havebeen composed of R(ZHH) and R(KDP).
•Here, Dm was used also in theselection of R(ZHH, Dm) or R(KDP, Dm).
)
29
Copyright 2009 by Eiichi Nakakita

Contents• In-Situ campaign observation synchronizedwithith Video-Sonde
Vid S d ((~ 5 min.)i )• A new operational QPE algorithm for C-bandpolarimetricl i t i radard (~( 10 min.)i )• Classification of co-existing hydrometeors usinga C-bandC b d polarimetricl ii radard (~( 14 min.)i )• Operational polarimetric radars in near futureby the Ministryi iof Land, Infrastructure,
Transportation and Tourism (MLIT), and one
off an iimportantt t backgroundb kd ((~20
20 min.)i )31
Copyright 2009 by Eiichi Nakakita

Datasets¾ When the typhoon 0723 approached on 27th Nov. ,six Video-sondes were launched.

Classification of co-existing hydrometeors
using a C-band polarimetric radar (2009)
Eiichi NAKAKITADisaster Prevention Research Institute (DPRI), Kyoto University

Yasuhiko SUMIDA
Graduate School of Engineering, Kyoto University

Kosei YAMAGUCHI
Institute for Sustainability Science (ISS), Kyoto University

Katsuhiro NAKAGAWA

Observation time 27th Nov.（JTC）

The number of particles

Start time

End time

Observation
time (min)

No 1
No.1

3：37

4：03

No.2

5：58

No 3
No.3

Ice
crystal

Snow
flake

Sum

27

11/27
5:30(JST)
17
125

107

5

254

6:23

26

10

447

1487

16

1960

6：53

7:19

27

42

38

45

6

131

No.4

7：58

8:15

19

21

2

1

0

24

N 5
No.5

10 51
10：51

11 16
11:16

26

91

8

7

0

106

No.6

11：36

11:57

22

132

78

64

0

274

313

698

1711

27

2749
33

National Institute of Information and Communications Technology (NICT)

Rain

sum

Kenji SUZUKI

GraupelDepartment of Agriculture, Yamaguchi University Copyright 2009 by Eiichi Nakakita

Hydrometeor Classification
Hydrometeor is classified into four types; rain, graupel, ice
crystalt l andd snow flake.
fl k
 The video-sonde observed the mixture of some types ofparticles over melting layer
layer.
 In previous many researches they usually classified just one
type of hydrometeor at each point
point.
 We consider that two types of hydrometeors mixed-exist, when
the difference in values of evaluation index between the highest
and second highest hydrometeors is small.
 G
Graupel+Ice
aupe ce ccrystal,
ys a , G
Graupel+Snow
aupe S o flake
a ea
and
d Ice
ce c
crystal+Snow
ys a S oflake, are set in addition to each types.

Copyright 2009 by Eiichi Nakakita

Hydrometeor Classification

Rain

j=1

p
Graupelj=2

Membership Function

Ice crystal

j=3

Snow flake

j=4



μ jZ

μ jZ

HH

DR

ZHH Membership Function μ j ZHHZDR Membership Function μ j ZDRρHV Membership Function

μ jρ

HV

KDP Membership Function μ K DP
j34
Copyright 2009 by Eiichi Nakakita

35
Copyright 2009 by Eiichi Nakakita

Hydrometeor classification Method (mixture)

Malting Layer Detection by ρHVMembership function
μj

Melting layer height Membership function

MLH

μj

Z HH

, μ j ZDR , μ j ρHV , μ j KDP , μ j MLH（ j : Particle type index）

Maltingg
layer
Icing layer(Graupel IC,(Graupel,
IC SF)

Rain

0
¾
¾
¾

Hydrometeor Evaluation index(

Qj = μ j MLH ( h ) × μ j ZHH ( Z HH ) + μ j ZDR ( Z DR ) + μ j ρHV ( ρHV ) + μ j KDP ( KDP )10

Decide Particle Types( j )It is important to distinguish between rain and icing layer.
layer
Melting layer height is detected by ρHV.
We make the Melting Layer Height (MLH) membership function

Hydrometeor Classification (mixture) (1)Rain
Graupel6000

Ice Crystal
Snow Flake（）
Heigghtm
Height(m)
H

5000

Ice
CrystalGraupel + Ice
CrystalMixture of hydrometeorsmax{ Qj ; j = 1, 4 } = Qjmax
if Qjmax－ Qjsec≦0 1≦0.1
then particle type : jmax and jsec
jjelse
particle type : jmax

36
Copyright 2009 by Eiichi Nakakita

7000

)

37

Copyright 2009 by Eiichi Nakakita

Hydrometeor Classification (mixture) (2)Graupel +Snow Flake

Snow Flake

4000

Rain

3000

雨

2000

0
0.0001

0.001

0.01

0.1

1
3

Mass Density(g/m
) 3）
Mass
Density（g/mCopyright 2009 by Eiichi Nakakita

10

Rain
n
Graupeel
Snow Flak
ke
G+IC
C
G+SF
F
IC+SF
F

1000

38

39
Copyright 2009 by Eiichi Nakakita

Current Radar network by MLIT in Japan

Contents• In-Situ campaign observation synchronizedwithith Video-Sonde
Vid S d ((~ 5 min.)i )• A new operational QPE algorithm for C-bandpolarimetricl i t i radard (~( 10 min.)i )• Classification of co-existing hydrometeors usinga C-bandC b d polarimetricl ii radard (~( 14 min.)i )• Operational polarimetric radars in near futureby the Ministryi iof Land, Infrastructure,
Transportation and Tourism (MLIT), and one
off an iimportantt t backgroundb kd ((~20
20 min.)i )C-band Conventional

Old type dual polarization
C-band full Polarimetric

f
from
A
April,
il 200940
Copyright 2009 by Eiichi Nakakita

Observed example by the new C-band full polerimetric radar（July 24,2009）

ZH

Copyright 2009 by Eiichi Nakakita

Observed example by the new C-band full polerimetric radar（July 24,2009）

ZV

φDP
偏波間位相差φDP

ZDR
42

Kyushu River Bureau, MLIT, 200943
Copyright
2009 by Eiichi Nakakita

ρHV
偏波間相関係数ρHV
Kyushu River Bureau, MLIT, 2009

Radar networks by MLIT in Japan in near future

One of the important background for installing
operational X-band polarimetric radar (2009)
C-band Conventional

Eiichi NAKAKITA

C-band full polarimetric

Hiroyukiy
YAMABE

Disaster Prevention Research Institute (DPRI), Kyoto University

f
from
A
April,
il 2009
C-band Doppler full polarimetricwithin 2
2-3
3 years

Graduate School of Engineering, Kyoto University

Kosei YAMAGUCHI
Institute for Sustainability Science (ISS)(ISS), Kyoto University

X-band Doppler full Polarimetricfrom Apri
Apri, 2010
Copyright 2009 by Eiichi Nakakita

Background
On July 28 （Toga River, Kobe)
Ab t 50 people
Aboutl were washed
h d away b
by th
the
flush flood in Toga River, Kobe, Japan,
without any overpass from embankment
embankment.
Five people were died.
In this casecase, many people were playing in theriver side. This is the place where public
people enjoy the water front. The local
people and Kobe government have been
making any efforts to develop such thewater front
front.
There is a risk that same disaster could occur
in the any urban small rivers
rivers.

Copyright 2009 by Eiichi Nakakita

Background

１４：３６

On August 5 (Zoshigaya, Tokyo)
Six people, working in an underground sewage
pipe system in Toshima ward, Tokyo, were
swept away and five people were died
died.
These disasters were occurred by isolated
cumulonimbus (isolated convective rainfall)

10 min.
later
１４：４６

Copyright 2009 by Eiichi Nakakita

Copyright 2009 by Eiichi Nakakita

Images from monitored movie managed by Kobe City
１４：２０

Becamedark

１４：３６ Ground was

１４：３８ Large size

begun tobe wetRain drop

１４：４０

Water level:－0.37m
１５：００

１５：１０

１５：４０

Water level:
1.05m

Water level:
0.52m

Water level:
0.28m

１４：５０

1.34 ｍ rise in
10 min.

View was blindedby rainOperationally distributed radar image
About 5~10 minute
14：25is14：20
necessary for the
information transmission.
You can get 14:20 radardata(radar image) at 14:30

14：30

Radar observation
at 14:20
Water level:－0.33m

orks wereere
１４ ４４ Side works
１４：４４
covered by water

１４：４２

W t llevell rising
Water
ii
began

水位：1 01m
水位：1.01m
１４：４６－９－
Copyright 2009 by Eiichi Nakakita

14:20

14：35

14：40

14:42
14:30

14:40

Time

9The rain area covered all the small river at 14：35:
flash flood
9Flush flood occurred at 14:42.
9Concentration time is very short.
short
9In this case, you may think that this 7 minutes is enough forpeople to escape . However you should not forget that we needtime for making and transmitting radar image.
image
9People can verify the radar image which shows us that the
14:42
rain area covered all the small river base is after flash occurred.Copyright 2009 by Eiichi Nakakita

The advantage of operational 3D scan
Height20km

The baby rain cell

5km
Cloud droplet cm-radar
can not detect

catch

catch

catch
Toga river

9Low elevation scan detect raindrops after the cumulonimbus growing up.
9The baby rain cell cannot be detected by a low elevation observation.
9The
Th volumelscan radard monitoringi i can detectdtheh babyb b stage off theh rainfall.
i f llCopyright 2009 by Eiichi Nakakita

６０ｋｍ

Copyright 2009 by Eiichi Nakakita

Baby rain-cell
generated at theheight of 5~6 km

30 min. before
flash flood
14:20

Toga river

Toga river

There is no precipitation
at theh groundd.

There is no precipitation
at theh groundd.

６０ｋｍ

Copyright 2009 by Eiichi Nakakita

14:22

６０ｋｍ

Copyright 2009 by Eiichi Nakakita

Toga river

The rainfall reached to thegroundd surface.
f
Copyright 2009 by Eiichi Nakakita

14:28

View from Osaka

Copyright 2009 by Eiichi Nakakita

The baby rain cell isgenerated in the upperg
pp
atmospheric layers.

Toga river

６０ｋｍ

Copyright 2009 by Eiichi Nakakita

Toga river

６０ｋｍ

Copyright 2009 by Eiichi Nakakita

14:20
１４：２０

Toga river

Becamedark

Water level:－0.37m

14:22

Toga river
14:28

６０ｋｍ

Copyright 2009 by Eiichi Nakakita

６０ｋｍ

Copyright 2009 by Eiichi Nakakita

１４：３６ Ground was

begun
g to
be wet１４：３８

Large sizepRain drop

Toga river

Toga river
14:42

６０ｋｍ

Copyright 2009 by Eiichi Nakakita

Toga river

６０ｋｍ

６０ｋｍ

Copyright 2009 by Eiichi Nakakita

Toga river

Copyright 2009 by Eiichi Nakakita

６０ｋｍ

Copyright 2009 by Eiichi Nakakita

Radar networks by MLIT in Japan in near future

Plan of installing operational X-band Doppler
Polarimetric radars over majors urban areas
by River Bureau, MLIT

C-band Conventional
C-band full polarimetric
富
富士

f
from
A
April,
il 2009
C-band Doppler full polarimetric

雲仙

within 2
2-3
3 years
X-band Doppler full Polarimetric

大隅from Apri
Apri, 2010

From April, 2010
From April
April, 2011 (proposal)
既存Xバンドレーダ

Copyright 2009 by Eiichi Nakakita

New operational network by X-band radars
60 km range
30 km
range

Kobe

Kyoto
Osaka

By the River Bureau in MLIT

•Higher sensitivity by :X band radar•Highergspatialpresolution byy :X band radar (250~500 m)
Dense network•Free from attenuation by :Polarimetric function (KDP)
Dense network together with C-band•Higher accuracy by :Polarimetric function (ZDR, KDP)
•Shorter scan interval with low
elevationl ti :
1 minutes•Shorter transmission time :2 minute•3D image :Volume scan

New operational networks by X band polarimetric Doppler radars are scheduled to bein operation in various urban areas in Japan before next April. Copyright 2009 by Eiichi Nakakita

国土交通省河川局

Copyright 2009 by Eiichi Nakakita

Concluding remarks• In-Situ campaign observations synchronizedwithith Video-Sonde
Vid S d hhave bbeen carriedi d out.
t• A new operational QPE algorithm for C-bandpolarimetricl i t i radard was developed.
d ld• Algorithm for classifying the co-exisingh dhydrometeors
using
i aC
C-bandb d polarimetricl iiRadar was developed• Plan for introducing
i
i operationalipolarimetriciiradars by MLIT is introduced with aniimportantt t background.
b k
d

67
Copyright 2009 by Eiichi Nakakita