Data Assimilation Experiments ofRadio Occultation Datausing JMA Meso-4dvar System-Impacts on the Heavy rainfall in Japanand the Development of TyphoonHiromu SEKO*, Yoshinori SHOJI*, Masaru KUNII**,and Toshitaka TSUDA****Meteorological Research Institute, Japan Meteorological Agency**University of Maryland** Research Institute for Sustainable Humanosphere, Kyoto University

Contents
1. Assimilation method of GPS radio
occultation data.
2. Synergistic improvement of the heavy
rainfall forecast using GPS RO dataand ground-based GPS data.
3. Summary.

Motivations• Heavy rainfall often occurs whenhumid airflow is supplied from the sea.
• Typhoons generated in low latitudes
also brought heavy rainfalls.
• Dry air at middle level enhances
convections.
• ‘Water vapor distribution over the sea’and ‘Vertical profile of water vapor’
are important for accurate forecasts.

Motivations• ‘Water vapor distribution over the sea’and ‘Vertical profile of water vapor’
are important for accurate forecasts.
• GPS radio occultation data is useful
data, because it provides theseinformation.
• Impacts of GPS radio occultation dataon forecasts of heavy rainfall andtyphoon are investigated.

1. Assimilation method of
GPS radio occultation data
1a. Torrential rainfall event.
1b. GPS radio occultation data.
1c. Assimilation method.
a. spherical un-uniform distribution(Tangent point data  Path data)b. without thinning of data
(Observation error covariance)Torrential rainfall on 16 July 2004

•Almost every year, heavy rainfalls
were caused by Baiu front.

Torrential rainfall on 16 July 2004

•Baiu front (stationary rainy front)that crossed the northern Japancaused the heavy rainfall.

Torrential rainfall on 16 July 2004Ohyu
133mm/day•At Ohyu, rainfall amount of 133mm/day
was observed.

GPS radio occultation data
CHAMP/ISDC (GFZ) :Challenging Mini-Satellite Payload
for Geoscientific Research and Application
Information System and Data Center

Level 1:Satellite orbit data
Surface reference
Station data
Level 2:Atmospheric phase delay,
Position and moving speed ofCHAMP and GPS satellite.
Level3(1):Profiles of refractive index,temperature and declination
Level3(2):Profile of water vapor

GPS radio occultation data
CHAMP/ISDC (GFZ) :Challenging Mini-Satellite Payload
for Geoscientific Research and Application
Information System and Data Center

Level3(1):Profiles ofrefractive index,...

Refractive index(RI):RI is a function ofe6 P
temperature and RI  1  77.6  10
 0.373 2
TTwater vapor.
RI is expected to improve rainfall forecasts.

Tangent point data

Tangent point

GPSSatelliteLEO
200m

Schematic illustration of estimation of the tangent point data

•Tangent point data was provided.
•Tangent point was the point closest to
the earth on the path.
•In the estimation of tangent point data,
spherical uniform distribution wasassumed.

Assumption of sphericaluniform distribution
Tangent point
Path data
GPSSatellite

=?

=?

=?
=?

LEO200m•Using the first guess data of thisheavy rainfall case, we investigated
the assumption of spherical uniform
distribution of RI.

Position of the lowest path(b) Vertical positionof path data
Height(a) Horizontal positionof path data

NE 300km
Ohyu
133mm/day

SW 300km
ir (element number on the path
1 element = 10 km)•RO occurred near the rainfall band at 12
JST 16 July, just before the occurrenceof heavy rainfall.
•Lowest path penetrated the rainfall band.

Distribution on lowest path
RIpath(z) - RItangentpoint(z)Tangent point
Path data=?

=?
=?

=?

RIpa RItp

D-value

GPSSatelliteRIpb

NNE 300km
Height 10km

RIpa- RItp

LEO

RIpb- RItp

SW 300km
Height 10km
Height 2.8km

Position of the lowest path(b) Vertical positionof path data
Height(a) Horizontal positionof path data

NE 300km
Ohyu
133mm/day

SW 300km
ir (element number on the path
1 element = 10 km)•Was this assumption of sphericaluniform distribution wassatisfied?

Assumption of sphericaluniform distribution
Tangent pointPath data
GPSSatellite

=?

=?

=?
=?

LEO

D-value

200m

NNE 300km
Height 10km
Height 2.8km•Assumption ofspherical uniform
distribution was
SW 300km
Height 10km
not accurate.

Assumption of sphericaluniform distribution
Tangent point
Path data
GPSSatellite

=?

=?

=?
=?

LEO

D-value

200m

NNE 300km
Height 10km
Height 2.8km•Then, wereproduceｄ thepath data that
does not need to
SW 300km
satisfy the
Height 10km
assumption.

Tangent point  Path data
RItp

Tangent point
Path data

3GPS
Satellite

=

RIp1

==

L3a L2a=L1LEO

L2a L3aRItp2

RItp1
L3a RI tp 3  L2 a RI tp 2  L1RI tp1  L2b RI tp 2  L3b RI tp 3
RI p1 L3a  L2 a  L1  L2b  L3b•Path data was reproduced from tangent
point data by path-length weighting
average.

First guess of Path data
Path data
GPSSatellite

Tangent point

LEO
200mn ( height 10 km )RI first _ guess _ of _ path  RIi 1

first _ guess _ of _ point

n-n is the total number of elements under the height of 10km.

•First guess was produced by averaging
RI on the path.

• Observed RI waslarger than that offirst guess below
5 km.
• The rainfalls isexpected to beintensified
by the assimilationof RO data.

Height(km)Observed and first guess
profiles of the path data

Blue：First guess
Red:Observed data

(RI-1）×106

Correlation of Observation Error•Observation error has verticalcorrelation.
(vertical resolution, path data)•In general, thinning of the data isperformed. However, thinning mightremove the informative data.
•Observation error covariance wasproduced and used in the assimilation.

Correlation of Observation Error•Vertical correlation was considered byfollowing Chen et al (2005).
(1) D-value of each height:
xi  DNi
obs _ err

 DNi
obs ( t )  mdl ( t )ti DN mdl( t  )  mdl ( t  12 )
12(2) Correlation of Obs. Err.:
rk1k 2 x k 1 xk 2  x k 1 x k 2

  x  x xk21  xk1

2

2k2

2

k2

(1)

NMC method( 2)

Correlation of Observation Error r1112 r211 22(3) Observation R  r121 2 r22 2
covariance: r  
 1n 1 n rn11 n   2
 rnn n 

(3)Obs. Err. of each height(a) Correlation of obs.err.(rij)(b) Simplified obs. covariance (R)2. Synergistic improvement
using GPS RO data andground-based GPS data
2a. Assimilation method ofground-based GPS data.
2b. Impact of GPS RO data andground-GPS data.

Ground-based GPS data
- Estimation of Zenith Total Delay• Zenith Total Delay
1. Zenith total delay (ZTD) is(ZTD) wasestimated by GPS Software(GIPSY)obtained by GPSsoftware (GIPSY) .
1.Slant total delay
was converted tovalue of zenith
direction.

Ground-based GPS data
- Estimation of Zenith Total Delay• Zenith Total Delay
1. Zenith total delay (ZTD) is(ZTD) was
estimated by GPS Software(GIPSY)
obtained by GPSsoftware (GIPSY) .
1.Slant total delay
was converted tovalue of zenith
direction.

Ground-based GPS data
- Estimation of Zenith Total Delay• Zenith Total Delay
1. Zenith total delay (ZTD) is(ZTD) wasestimated by GPS Software(GIPSY)obtained by GPSsoftware (GIPSY) .
2. By applying themapping factor,
which is model ofdelay distribution,
ZTD was estimated.

Ground-based GPS data
- Estimation of Zenith Total Delay• Zenith Total Delay
1. Zenith total delay (ZTD) is(ZTD) wasestimated by GPS Software(GIPSY)obtained by GPS
mappingsoftware(GIPSY).
function
2. By applying themapping factor,
which is model ofdelay distribution,
ZTD was estimated.

Ground-based GPS data
- Estimation of Zenith Total Delay• Zenith Total Delay
1. Zenith total delay (ZTD) is(ZTD) wasestimated by GPS Software(GIPSY)obtained by GPSsoftware (GIPSY) .
ZTD

2. By applying themapping factor,
which is model ofdelay distribution,
ZTD was estimated.

Ground-based GPS data
- Estimation of Zenith Total Delay• Zenith Total Delay
1. Zenith total delay (ZTD) is(ZTD) was
estimated by GPS Software(GIPSY)obtained by GPS
residual software (GIPSY) .
3. Residuals, thedifferences of STD
from the modeled
delay, are also
obtained.

Ground-based GPS data
- Estimation of Slant Total Delay1. Slant total delay (STD) isestimated from ZTD,
mapping function etc.

• Zenith Total Delay(ZTD) wasobtained by GPSsoftware (GIPSY) .
4.We retrieved STDfrom ZTD,
mapping function
and residuals.

Ground-based GPS data
- Estimation of Slant Total Delay1. Slant total delay (STD) isestimated from ZTD,
mapping function etc.

• Zenith Total Delay(ZTD) wasobtained by GPSsoftware (GIPSY) .
4.We retrieved STDfrom ZTD,
mapping function
and residuals.

ZTD,STD  PWV,SWV• PWV is estimated using surface pressure andtemperature.

ZTD  ZHD ZWDRZHD  10g m Psfc
md6

 z g z dzg   z dz
mZWD  Π 1  PWV105 mv k1  k2  k1
Π 
md Tm R Pv
 T dzTm Pv
 T 2 dzTm  70.2  0.72Tsfc• SWV is estimated using the same proceduresfrom STD.

Paths of RO and ground-GPS datalllll

PWVll

SWVl

• First guess was produced by integrating ofwater vapor along the path.
• Influence of difference of altitudes wascorrected by the observed surface pressure
and temperature.

Positions of RO andground-based GPS data(a)

• Grid distribution ofwhich spatial
interpolation
weights ere
positive in theassimilationsof ground-based
PWV GPS data (PWV,PWV
SWVSWVSWV) and RO data.
ROROD-values of ground-based
GPS data
Obs.>First guess

Obs.<First guess

Obs.<First guess•D-value ofPWV (Obs.First guess)from 12 JST
to 15 JST.

Rainfalls region predicted fromanalyzed fields (conventional data)(a) Observation(b) CNTL

Three hour rainfall of forecast time from 0 to 3 hours reproduced fromthe analyzed fields. Valid time is 15 to 18 JST 16 July 2004.

Heavy rainfall was not reproduced byassimilation of conventional data.

Rainfalls region predicted fromanalyzed fields (GPS data)(a) RO(b) PWV(c) SWV(d) RO+PWV(e) RO+SWV

Obs.

(mm)

(mm)

(m/s)

Reproduced 3-hour rainfall of forecast time from 0 to 3 hours reproduced from theanalyzed fields. Valid time is 15 to 18 JST 16 July 2004.

Rainfalls region predicted fromassimilated field of RO
Lowest path

Height(km)

2.0
1.5
1.0
0.5
0.1
-0.1
-0.5
-1.0
-1.5
-2.0(g/kg)

Reproduced 3-hour rainfall and increment of Qv• Rainfall was intensified as it isexpected by the D-value of RI
profile.

Blue：First guess
Red:Observed data(）

6

Rainfalls region predicted fromassimilated field of RO

Height(km)

2.0
1.5
1.0
0.5
0.1
-0.1
-0.5
-1.0
-1.5
-2.0
(g/kg)

Reproduced 3-hour rainfall and increment of Qv• But, Eastern part is too strong.
• Moist SE-ly flow enhanced theintensity of the rainfall.

Blue：First guess
Red:Observed data(）

6

Rainfalls region predicted fromassimilated field of PWV
2.0
1.5
1.0
0.5
0.1
-0.1
-0.5
-1.0
-1.5
-2.0(g/kg)

Reproduced 3-hour rainfall and increment of Qv• Rainfall was intensified as it isexpected by the D-value
distribution of ground-based GPS.

Rainfalls region predicted fromassimilated field of PWV
2.0
1.5
1.0
0.5
0.1
-0.1
-0.5
-1.0
-1.5
-2.0(g/kg)

Reproduced 3-hour rainfall and increment of Qv• Increment of water vapor isconsistent with d-value
distribution.

Rainfalls region predicted fromassimilated field of PWV
2.0
1.5
1.0
0.5
0.1
-0.1
-0.5
-1.0
-1.5
-2.0
(g/kg)

Reproduced 3-hour rainfall and increment of Qv• Eastern part was weaker than RO.
• Weak SE-ly flow reduced 3hourrainfall, though water vapor wasincreased.

Rainfalls region predicted fromassimilated field of SWV
PWV

SWV

Reproduced 3-hour rainfall and increment of Qv• Eastern part of heavy rainfall
was weaker.

Rainfalls region predicted fromassimilated field of SWV
SWV

PWV
2.0
1.5
1.0
0.5
0.1
-0.1
-0.5
-1.0
-1.5-2.0(g/kg)

2.0
1.5
1.0
0.5
0.1
-0.1
-0.5
-1.0
-1.5
-2.0
(g/kg)

Reproduced 3-hour rainfall and increment of Qv• Increment became moresignificant when SWV was used.
• Water vapor on eastern side ofnorthern Japan was decreased.

Rainfalls region predicted fromassimilated field of SWV
SWVPWV
2.0
1.5
1.0
0.5
0.1
-0.1
-0.5
-1.0
-1.5
-2.0(g/kg)

2.01.51.0
0.5
0.1-0.1-0.5-1.0-1.5-2.0(g/kg)

Reproduced 3-hour rainfall and increment of Qv•How are vertical crosssections modified?

Vertical distribution ofincrements of water vapor
10(a) RO

1010(b) PWV

88(c) SWV

2.0
1.5

8

1.0
0.5

6664442220.1
-0.1
-0.5
-1.0
-1.5
-2.0(g/kg)
3436384042N 32
Latitude

Height(km)32

Blue：First guess
Red:Observed data(RI-1）×106

34363840

42N 32
Latitude3436384042N
Latitude• When RO data was assimilated,water vapor was increased
at the low-level.
• SWV modified the vertical
distribution, but not so large.

Synergistic improvement using
RO and ground-based GPS data(d) RO+PWV(b) PWV(e) RO+SWV(c) SWV(a) RO(mm)

(mm)

(m/s)

•When both data were assimilated, therainfall regions had both features of RO
and ground-based GPS data.

Synergistic improvement using
RO and ground-based GPS data(d) RO+PWV(b) PWV(e) RO+SWV(c) SWV(a) RO

2.0
1.5
1.0
0.5
0.1-0.1-0.5-1.0-1.5-2.0(g/kg)•When both data were assimilated, theincrements had both features of RO andground-based GPS data.

10Synergistic improvement using
RO and ground-based GPS data

810(a) RO

864232

3436

38

40

42N
2.0
1.5
1.0
0.5
0.1
-0.1
-0.5
-1.0
-1.5
-2.0(g/kg)

10(d)RO+PWV

866442234

32
10836

38

4042N(e) RO+SWV

32
10866442232

3436384042N(b) PWV

3436384042N

384042N(c) SWV323436•When both data were assimilated, theincrements had both features of RO andground-based GPS data.

Synergistic improvement using
RO and ground-based GPS data(d) RO+PWV(b) PWV(e) RO+SWV(c) SWV(a) RO

2.0
1.5
1.0
0.5
0.1
-0.1
-0.5
-1.0
-1.5
-2.0
(g/kg)•When RO and SWV data were assimilated,
the rainfall region was most similar to theobserved one.

•Summary• Assimilation method of path data wasdeveloped. In this method, the verticalcorrelation of observation error wasconsidered.
• When RO data and ground-based GPS
data are assimilated simultaneously,
rainfall forecast and water vapor fields
are much improved.