Data Assimilation Experiment ofa Heavy Rainfall at Nerima / Tokyo
Hiromu Seko, Takuya Kawabata （MRI/JMA),
Tadashi Tsuyuki (NPD/JMA)
Key factors of generation and development of the convections:
1. Low-level convergence : Doppler-radar, wind-profiler etc.
2. Moist air supply : Sonde, GPS, SSM/I, TMI eｔｃ.
Kobayashiet al., 2000

ＧＰＳ Satellite

Rain droplets
Radar
ＧＰＳ Receiver
Moist airflow

Nerima heavy rainfall on 21 July 1999

115.5mm/hr

1６JST

Hourly precipitation amount,temperature (grey line) andconvergence (red line) at 16JST

1213141516Red: Precipitation
Green：temperature
Yellow：Sun shine

1718JST

Hourly precipitation of 115.5mm
was observed at Nerima, Tokyo

Nerima heavy rainfall on 21 July 1999
Surface weather chart at 09JST 7/21
(09JST=00UTC)・Baiu front crossed the northern part ofJapan at 09JST.
Kanto

・ Precipitation region of Baiu front moved

southward and reached Kanto region at15JST.

Precipitation observed by operational radars 09-21JST 7/21

9JST

12JST

15JST

18JST

21JST

Reflectivity fields and horizontal wind
Reflectivity (dBZ) of Haneda airport radar and horizontal
wind at z=1km and 4km obtained by Dual radar analysis

z=4km
1500JST

1529JST

1608JST

Middle-level
airflow intruded
convections

Precipitationof Baiu front
Convections caused
heavy rainfall

z=1km
1500JST

Convergence of S-ly flow
generated the convections,

1529JST

1608JST

Middle-level airflow
produced intense outflow.

Surf_P・Echo

GPS-derived
PWV
Surf_T・Echo
CW

14JST
Surf_Qv

14JST

PWV already began
to increase PWV/Surf_Qv
beforethe generation.

LW

15JST

L

Surf_P・Echo

W

PWV/Surf_Qv

Warm air flow
was converged

15JST

Cycloniccirculation wasestablished

15JST

15JST

Western part ofsystem became lineshaped due to thecold outflow

16JST

16JST

Intense cold
outflow diverged

17JSTHCCW
16JST

W

L

16JST

HCW

17JST 17JST

Increment PWVsuggests themovement ofprecipitation area

17JST

Radial wind data

uniform

VVP method

Radial Wind

Dual analysis

not uniform

Area where the horizontal wind is estimated:
1. VVP method
Area, where the horizontal wind can beassumed to be varied uniformly, in whole range of a radar
2. Dual analysis
Part of area where the ranges of more than
two radars are overlapped.
These areas are smaller than whole range of radars.

Doppler radar radial wind data

Vr (Haneda15JST）

Super observation method
Number of raw data >> number of model grid points.

Radial wind data：Positions ： Grid positions of numerical models on eachelevation cone.
Interpolation method ： Cressman method.
Range for one data：raw data on the cone within the range of the model grids
during 10min before each hours. Position ofm/s

Vr _ obs x0  x / 2

y 0  y / 2

 w Vr / x  x0  x / 2 y  y 0  y / 2w

R2  D2R2  D2x0  x / 2y 0  y / 2w

radial wind dataHeightx  x0  x / 2 y  y 0  y / 2Radar

Raw data within 10km
from the radar is not used.

Grid position ofnumerical models

where, D is distance between the radial wind data point and raw data.
R is set to 5km.
Elevation range ： 0.0 - 5.4 deg. (where the vertical velocity can be neglected)

Observation operator
Simulated radial wind were estimated from grid point values withthe weight of the radar beam intensity.
（It is assumed that radar beam intensity has Gauss distribution.）x0  x / 2

Vr_model y 0  y / 2 beam 1 w Vr _ grid /  x  x 0   x / 2 y  y 0   y / 2  beam 1 R D

2R D22w

22ex0  x / 2

  gridl  beam  log e 2
 h h  1 .27  / A / 2 ( rad ) beam 1 w
 x  x 0   x / 2 y  y 0   y / 2  beam 1 

Grid position of models
Model grids within the radar beam
Position ofradial wind

where、R : radius of influence,D : distance between modelgrids and radial wind data,
A : Diameter of antenna.
Beam cut due to the mountains
is taken into consideration.

y 0  y / 2

HeightWhen the beam is narrow, thegrids of upper and lower sides
are used.

More than two grids existed
within radar beam.

GPS water vapor data・Phase delay of signals (slant delay) was obtained from GEONET data.
・Precipitable water vapor (PWV) and Slant water vapor (SWV) wereestimated from slant delay with surface pressure and temperature.
・Influence of Multipath and PCV was removed (Iwabuchi et al.2004;Shoji et al. 2004).
Precipitablewater vapor

SWV has information of
3D water vapor distribution.

GPS water vapor data・Phase delay of signals (slant delay) was obtained fromGEONET data of GSI.
・Precipitable water vapor (PWV) and Slant water vapor (SWV) were
estimated from slant delay with surface pressure and temperature.

Observation operator
・Path was traced from GPS receiver positions by the ray
tracing method.
・Density  and specific humidity qv were estimated by
Cressman interpolation method.
・SWV is estimated by integration of  ×qv.
Swv _ mdl GPS satellitei  i _ top ( z 15 km )

 li  q vi 1

・The GPS data of which altitude was less than
50m from the altitude of a model was used.

l3

l4

l2

l1

GPS receiver

HeightChallenge:
・Precipitation region of Baiu front moved into the ranges of Doppler
radars at 15JST
・Conventions at southern Kanto was just generated at 15JST
・We want to reproduce the precipitation at the southern Kanto byusing the data from 9JST to 15JST with MSM-4DVar system.

9JST

12JST

15JST

Period ofassimilation(9-15JST)
Period offorecast(15JST-)

16JST

17JST

18JST

19JST

Radar-derived rainfall intensity （1999 7/21 09-21JST)
Circles indicate the range of the Doppler radars

First Guess of MSM 7/21 9-21JST (without assimilation)
Kanto
9JST

10JST

11JST

12JST
Precipitation andhorizontal wind at z=1.5km
Red region >1mm/hour.

13JST

14JST

15JST

10m/s

Numerical Forecast Model:
Mesoscale Model (MSM) of JMA
・Grid interval：10km (INNER：20km)
18JST

21JST

・Simulated Baiu front stayed north of Kanto ( ).

Verification of precipitation（16-18JST）
Radar

16JST

First guess

Conventional data

16JST

FT=01

Vr

Hourly precipitationand horizontal windat z=0.5km

・When the radialwind wasassimilated,
FT=01
N-ly wind wasreproduced andprecipitation
was generated.
・ However the17JST

17JST

FT=02

FT=02

18JST

18JST

FT=03

FT=03

development
was delayedfor 1hour.

5m/s

・When assimilation was not performed,
the southwesterly wind predominated.

・When sonde data was assimilated,
E-ly flow was simulated. However,precipitation was not reproduced.

Verification of precipitation（16－18ＪＳＴ）
Radar

Conventional data

PWV

Vr & PWV

SWV

Vr & SWV

16JST

FT=01

FT=01

FT=01

FT=01

FT=01

17JST

FT=02

FT=02

FT=02

FT=02

FT=02

18JST

FT=03

FT=03

FT=03

FT=03

FT=03

・When GPS data was assimilated,
the precipitation was generated.
However, its position was shifted to the east.

・When both data were used, theprecipitation was generated
where it was observed.

Verification of precipitation （16-21JST）
Observation(Radar)Vr & PWV

Observation(Radar)

Vr & PWV

16JST

FT=01

19JST

FT=04

17JST

FT=02

20JST

FT=05

18JST

FT=03

21JST

FT=065m/s

・When Vr and PWV data were used, the precipitation was well reproduced
until 21JST (FT=06hr).

Effective height of radial wind data
Radar

Vr23 (6.5km)

Hourly precipitation andhorizontal wind at z=0.5km

Vr10(1.0km)

Vr17 (3.5km)

１６JST

FT=01

FT=01

FT=01

１７JST

FT=02

FT=02

FT=02

FT=03

FT=03

FT=03

１８JST

5m/s

・Even radial wind at z<1km was assimilated, the precipitation. was reproduced.
→ In this case, the low-level flow is important for the generation of the precipitation

Influence of the radial wind range
RadarVr23 & PWVHANEDA

Hourly precipitation andhorizontal wind at z=0.5km
NARITA

NARITA

16JST

FT=01

FT=01

FT=01

17JST

FT=02

FT=02

FT=02

18JST

FT=03

FT=03

FT=03

HANEDA

5m/s

・N-ly wind was reproduced by low-level radial wind observed by HANEDA radar.
(NARITA data can not detect N-ly flow that existed below the cone of lowest elevation.)→ It also indicates the importance of the low-level flow.

Surface wind and water vapor of analyzed fields
First guess

Difference from First guess
Conventional data

PWV

SWV

Vr23

Vr23 & PWV

Vr23 & SWV

10m/s

・On the southern part ofKanto （ ）,low water vapor region
existed・In all experiments,
water vapor of southern
Kanto is increased.

・S-ly flow was weakened where water vapor increased.
→This change of the horizontal wind intensifies the low-level convergence andmoves the convergence southward.

4m/sSurface wind and water vapor of analyzed fields(a) Conventional data(c)-(a)(c) PWV(b) -(a)(d)-(a)(d)-(c)(b) Vr23

・Radial wind(GPS water vapor)increases the watervapor at southwest ()(southeast()) part ofKanto.
・The difference between
each experimentindicates the influence ofradial wind and GPS data.

(d)-(b)・Difference indicates thefollowing relation;
Convergence - Increase
Divergence - Decrease→・Water vapor changes
the horizontal wind.
・ Horizontal windchanges the watervapor distribution.

(d) Vr23& PWV4m/s

First guess

B

５００

Benefit of SWV data６００(N-S cross sections ofanalyzed water vapor)

７００
８００
９００A

B

10m/s

A・PWV increases the water vapor
at whole layers.

Difference from first guess(water vapor and horizontal wind)
５００Vr

Conventional data

・Slant water vapor decreases
the middle-level water vapor.
(More realistic distribution,compared with PWV case)

10m/s

６００
７００
８００
９００A
５００B

PWVAB

Effect of slant paths

4m/s

Vr&SWV-Vr&PWV

Vr &SWV

Vr &PWV

５００
６００

６００

７００

７００

８００

８００

９００

９００AB

AB

AB

AB
4m/s

PWV increased the water vapor withkeeping their vertical features.

Slant data decreased the middle-levelwater vapor.

Verification of precipitation （16-21JST）
Observation(Radar)Vr & PWV

Observation(Radar)Vr & PWV

16JST

FT=01

19JST

FT=04

17JST

FT=02

20JST

FT=05

18JST

FT=03

21JST

FT=06

・Precipitation was well reproduced until 21JST (FT=06hr).
・To reproduce the intensity,
PWV and Vr were assimilated into NHM by 3DVar system.

5m/sNHM-3DVar
NHM-3dvar analysis fields from firstguessConventional data＋First guess

NHM-3DVar Forecast from Meso4DVar results

UV・Qv at z=1.5km

Surf_UV・Qv

Vr＋PWV• When the PWV and Vr were assimilated,
the northerly wind and moist inflow was reproduced.
• However, the precipitation was not reproduced at Kanto region.
• The horizontal interval of GPS station was about 25km.
• This interval was too long to express the convective scalewater vapor distribution.

Observed PWV

Convective scalewater vapor

Introduction ofprecipitation

Unsaturated
downdraft

Updraft was moistened by Precipitation weaken the Evaporation in downdraft
relation between updraft and updraft. (One of features produces intense outflow.
relative humidity.
of decaying convection).
100

RH in precipitation regions
was set according to therelation of 'RH' and 'w'(red line in the right panel).

90
RH（％）

Relation between
Updraft and RH
within rainfall area

80
70
60
50
-1.5

-1-0.500.5

1Scatter gram from simulated data of12JST initial experiments.

1.5

2w（m/s）

Effect of convective scale water vapor
Observation+ Moistened
updraft+Precipitation(rain and snow)+Modified RH
in downdraft

16JST

16JST(FT=1)

16JST(FT=1)

16JST(FT=1)

17JST

17JST(FT=2)

17JST(FT=2)

17JST(FT=2)

When updraft regions were moistened,⇒Distribution of convection became close to observed one.
When precipitation and relation of RH and w were introduced,⇒Decaying convection was reproduced ( ).
⇒Development due to outflow was also reproduced ( ) .

Simulated precipitation with NHM (x=2km)

17JST(FT=2)

16JST(FT=1)

Radar

z=20m

z=1km

z=5km

Precipitationof Baiu front.

Convections
caused heavy
rainfall.

Convergence of SW-ly flow
generated the convections.

Middle-level
airflow intruded
convections.

19JST(FT=4)

Middle-level airflow
produced intense outflow.

mm/hPT simulated with NHM (x=2km)

16.5JST(FT=1.5)

15.5JST(FT=0.5)

PSEA(hPa)

z=20m

PT-PT(t=0)

z=5km

Low

ColdCold

16.5JST(FT=1.5)

15.5JST(FT=0.5)

Qｖ simulated with NHM (x=2km)
PSEA(hPa)N

z=20m

Qv-Qv(t=0)

z=5km

Low

SIncrease

NDecrease
Decrease

SIncreaseVertical cross section of PT and Qv (x=2km)
15.5JST(FT=0.5)

PT-PT(t=0)

Qv-Qv(t=0)

（ｋｍ）

8
7
654
32
1（ｋｍ）

S-5N-3-2-1 -.5.5

1235 (K)

16.5JST(FT=1.5)

（ｋｍ）

8
7
65432
1

8
7
654
32
1S-5N-3-2-1 -.5.5

1235 (g/kg)（ｋｍ）

S-5N-3-2-1 -.5.5

1235 (K)

87654
32
1S-5-3N-2-1 -.5.5

12・Convections of heavy rainfall were generated before the arrival ofcold dry air at middle level.

35 (g/kg)

Summary
１．Doppler radar radial wind and GPS water vapor areuseful data for the short-range forecast of the heavy rainfall.
2. Introducing of convective scale water vapor and precipitation
help reproduce the heavy rainfall with NHM
3．The more realistic vertical profile is analyzed, when SWV wasassimilated.
4. In this heavy rainfall case
There are 2 stages in the development of the heavy rainfall(1) Convergence of humid low-level inflow generated theheavy rainfall .
(2) Middle-level cold rear inflow enhanced the heavy rainfall.

Acknowledgements
The authors thank Dr. Y.-H. Kuo, Dr. T. Iwabuchi and member ofCOSMIC / University Corporation for Atmospheric Research, Mr.
Y. Shoji and members of GPS/MET Japan project, for providing
the useful comments and suggestions.
Our thanks extend to Drs. K. Koizumi, T. Miyoshi, S. Nishijima,Y. Ishikawa, K. Akaeda of JMA, Mr. Y. Tanaka and the staff of GSI
for providing data assimilation programs, Doppler radar data,
the decode programs of the Doppler data and GPS data.