Development ofa Two-way Nested LETKF System
for Cloud Resolving Model
H. Seko, T. Tsuyuki, K. Saito, M. Kunii (JMA/MRI),
and T. Miyoshi（RIKEN)

Baiu front••

Typhoon

Almost every years, heavy rainfalls of Baiu fronts
and typhoons cause floods and landslides in Japan.
Predictions of these phenomena are relatively
easier because these rainfalls are caused by large
scale convergence or topography.

Local heavy rainfall

Tornado•Besides Baiu fronts and typhoons, local heavy
rainfall and tornados cause disasters.
• Predictions of these phenomena are difficult
because they are generated in weak convergence.
Accuracy of their forecasts should be improved(Data assimilation. Probabilistic forecast)→ Cloud resolving two way nested LETKF system.

Necessity ofa nested assimilation system
・Local heavy rainfalls cause urban flash floods.
・To reproduce local heavy rainfalls, mesoscale environmentand convection cells should be reproduced simultaneously
by the numerical models with large and small gridintervals.

Necessity ofa nested assimilation system
・Local heavy rainfalls cause urban flash floods.
・To reproduce local heavy rainfalls, mesoscale environment andconvection cells should be reproduced simultaneously by thenumerical models with large and small grid intervals.
・An X-band radar network and a
Ministry of Land,
GPS network have been established Infrastructure,
Transport andin Japan. This high resolution data Tourism
can be used as assimilation data to
improve the intensity and positionsof convection cells.

Field Experiments(TOMACS)
A field campaign in the Tokyo metropolitan area with a dense observation
network is being performed by MRI and thirteen groups in the summers of
2011-2013.
MRI C-band
Solid-state MP
Radar

Ku-band Fast Scan MP
Radar

2DVD Radar
Calibration site

MTSAT
Rapid Scan

Ｘ－ＮＥＴ
(NIEDS and cooperative org.)

JMA Operation
Doppler Radars

Scintillation
Meter
Dense Surface

Doppler Lidars

Network
JMA Lightning
Detection Systems

Microwave
radiometers

Wind Profilers

GPS Receivers

Radiosonde

Humid Warm Air

Relatively Cool Air

6

Necessity ofa nested assimilation system
・Local heavy rainfalls cause urban flash floods.
・To reproduce local heavy rainfalls, mesoscale environment andconvection cells should be reproduced simultaneously by thenumerical models with large and small grid intervals.
・An X-band radar network and a GPS network have been
established in Japan. This high resolution data can be used asassimilation data to improve the intensity and positions ofconvection cells.
・LETKF (Local Ensemble Transform Kalman Filter;
Miyoshi and Aranami, 2006) was used in this study. To
reflect the results of the inner LETKF in the outer model,
a two-way nested system has been developed.

Necessity ofa nested assimilation system
・Thunderstorm occurred on 31st July
2007, which was one of targets of
B08RDP Tier2 experiments was
reproduced by NHM.
・Rainfall system extending north-south
direction passed Beijing City and3-hour rainfall of 70 mm was observed
there.
・Rainfall intensity and rainfall amount
were reproduced by the NHM with gridinterval of 2km (Tier2). Initial andboundary conditions of Tier-2 were
produced from the output of the NHM
with grid interval of 15km (Tier-1).
・Rainfall intensity and rainfall amountwith the grid interval of 15km were
produced from outputs of 2km, andcompared with the results of Tier1.

GMS IR 18UTC 30 July 2007

RainfallM02p

15km grid

3km grid

Radar reflectivity

M03m

15km grid

3km grid

15km grid
3km grid

Maximum rainfall in Tier-2 domain
Maximum 3hour rainfall

3hour rainfall/grind (mm)3 hour rainfall amount(accumulated in Tier-2 domain)

membermemberNumber of grids
where rainfall exceeded 1.0mm
15km grid
3km grid

80
70
60
50
40
30
20
10
0

M00_03
M00_15
M03_03
M03_15
P02_03
P02_15

1

101

201

301

401

501

601
rank

rank

Rainfall area (grid)

3hour rainfall (mm)Rank of 3hour rainfall amount3hour rainfall (mm)15km grid
3km grid

member・Except P02, rainfall amounts of Tier-1（15km） were larger than those of Tier-2（km）.
・Maximum of rainfalls of Tier-2（2km） was larger than those of Tier-1（15km）.
・Grid numbers where rainfall exceeds1.0mm of Tier-1（15km）were wider those ofTier-2（2km）.
・Histogram indicates that rainfall was more concentrated in Tier-2（2km）.
・Rainfall amounts and intensities of Tier-2 (2km) can not be simply estimated fromthose of Tier-1（15km）.

Sakai-city intense rainfall eventon 5th Sep. 2008
Rainfall region
extendednortheastward

Sakai

14JST
15JST
Echo distribution observedby convectional radars (5th Sep. 2008)

16JST

Ten minutes rainfall amounts
from 14 JST to 17 JST at
Sakai-city.

32C

W

L

C

14JST

14JST

• There was a high temperature area at Osakaand a thermodynamic low system was generated here.
• A northeasterly flow and a southerly flow were convergedat Osaka Plain.

Airflow Structure of heavy rainfall
2.5km

1.5km

0.5km
Div

Div

Updraft was located on
NE side of thunderstorm

Downdraft exited in SW side

2.5km
Conv.

3.5km

L

Conv
Osaka bay

Warm
Cold

Schematic illustration of airflow
structure(Left) Horizontal wind at 1520JST obtained by Dual analysis from Osaka andKanku radar data. (Right) Vertical wind distribution at 1520JST.

Results of Single LETKF（5km-NHM)
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Results of Single LETKF（5km-NHM)
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Rainfall regions were
scattered among the members.
Thunderstorm was
reproduced#016#017 in only one#018
member #005.

#019

Experiments with changing amplitudes of deviations
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RMSmax_Tsurf=7.4K→5K

RMSmax_Tsurf=7.4K→7.4K

Reason why thunderstorm was not reproduced
PWV, T …

Obs

#002

#001

Position of thunderstorm was
not reproduced by changing
Threshold ofthunderstorm
deviation amplitude.
generation

Ave.
Position.

More assimilation data isneededNHM-3DVar（JNoVA0）(Miyoshi ,2003,Seko 2008)

Assimilation of Radial wind data
Doppler radars

LETKF
Grid interval is too coarse⇒updraft is weakincrement

Increment of surface temperatureand horizontal wind
NHM-3DVar
Updraft is modified by assimilationof Doppler radar data

When Doppler radar data
was assimilated, initialconvergence was intensified.

Assimilation of Radial wind data

NHM-3DVar

LETKF
15JST

When Doppler radar
data was assimilated,
the location of local
heavy rainfall was
improved.
17JST

16JST

no_radarno_radarno_radar

16JST

14JST

15JST

16JST
15JST
obs

radar
obs

17JST

radar
obs

radar

Convective scale water vapor

Convective scale water vapor

Loading effect of rainfall

15JST

16JST

Radar

Evaporation in downdraft
17JST

radar

Radar

16JST

14JST

15JST

16JST
15JST
obs

+Con
obs

17JST

+Con
obs

+Con

Forecast from assimilated fields (17JST)
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Forecast from assimilated fields (17JST)
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To reproduce the thunderstorm,
the surrounding environment, suchas the position of large scaleconvergence, should be also
reproduced simultaneously.

Schematic of nested LETKF system
6hour analysis window
observation data(every hour)
LETKF
cda data (surface + upper)
analysis

Forecast-Ensemble member :20(12)-grid interval 1.875 km

1 hour assimilation window
observation data (10minutes interval)
cda data (surface + upper),GPS etc.

boundary

LETKF
analysis

Reflection in outer
LETKF

boundary

downscale
Reflection inouter LETKF

Downscale

Forecast-Ensemble member 20(12)-Grid interval:15 km

6hour analysis window
6hour analysis window
observation data(every hour) LETKF observation data(every hour)
cda data (surface + upper) analysis cda data (surface + upper)

1 hour assimilation window
observation data (10minutes
interval)
cda data (surface + upper),GPS etc

・The outer LETKF with a grid interval of 15 km
assimilates the convectional data of Japan
Meteorological Agency. Assimilation window was 6hours.

Schematic of nested LETKF system
6hour analysis window
observation data(every hour)
LETKF
cda data (surface + upper)
analysis

Forecast-Ensemble member :20(12)-grid interval 1.875 km

1 hour assimilation window
observation data (10minutes interval)
cda data (surface + upper),GPS etc.

boundary

LETKF
analysis

Reflection in outer
LETKF

boundary

Downscale
Reflection inouter LETKF

Downscale

Forecast-Ensemble member 20(12)-Grid interval:15 km

6hour analysis window
6hour analysis window
observation data(every hour) LETKF observation data(every hour)
cda data (surface + upper) analysis cda data (surface + upper)

1 hour assimilation window
observation data (10minutes
interval)
cda data (surface + upper),GPS etc

・Inner LETKFs were deployed in the outer LETKF.
・Boundaries of the inner LETKFs were overlapped toreduce the regions that were determined by the outer
LETKF.

Schematic of nested LETKF system
6hour analysis window
observation data(every hour)
LETKF
cda data (surface + upper)
analysis-Ensemble member :20(12)-grid interval 1.875 km

boundary

LETKF
analysis

Reflection in outer
LETKF

Forecast

boundary

downscale
Reflection inouter LETKF

Downscale

Forecast-Ensemble member 20(12)-Grid interval:15 km

6hour analysis window
6hour analysis window
observation data(every hour) LETKF observation data(every hour)
cda data (surface + upper) analysis cda data (surface + upper)

1 hour assimilation window
1 hour assimilation window observation
observation data (10minutes interval)
data (10minutes interval)
cda data (surface + upper),GPS etc.
cda data (surface + upper),GPS etc

・The grid interval of the inner LETKF with grid interval
is about 2 km. The inner LETKF assimilated theconvectional data and high resolution data.
Assimilation window was 1 hour.

Schematic of nested LETKF system
6hour analysis window
observation data(every hour)
LETKF
cda data (surface + upper)
analysis

Forecast-Ensemble member :20(12)-grid interval 1.875 km

1 hour assimilation window
observation data (10minutes interval)
cda data (surface + upper),GPS etc.

boundary

LETKF
analysis

Reflection in outer
LETKF

boundary

downscale
Reflection inouter LETKF

Downscale

Forecast-Ensemble member 20(12)-Grid interval:15 km

6hour analysis window
6hour analysis window
observation data(every hour) LETKF observation data(every hour)
cda data (surface + upper) analysis cda data (surface + upper)

1 hour assimilation window
observation data (10minutes
interval)
cda data (surface + upper),GPS etc

・Results of the inner LETKFs were reflected in the outer
LETKF every 6 hours.
・Initial boundaries of the inner LETKFs were produced
from forecasts of the outer LETKF.

Results of nested LETKF system

6hour analysis window
6hour analysis window
6hour analysis window
observation data(every hour) 03JST 5th observation data(every hour) 09JST 5thobservation data(every hour)
LETKF
LETKF
cda data (surface + upper)
cda data (surface + upper)
cda data (surface + upper)
analysisanalysis

LETKF
analysisboundary-Ensemble member :20(12)-grid interval 1.875 km
1 hour assimilation window observation data
(10minutes interval)
cda data (surface + upper),GPS etc.

Ensemble mean (15JST,15km)

1 hour assimilation window observation
data (10minutes interval)
cda data (surface + upper),GPS etc

Spread (15JST,15km)• Spread at the center ofthe domain was
relatively large.
• So we deployed theinner LETKF at thecenter of the domain.

Reflection inouter LEKF

downscalboundarye
Reflectioninouter LETKF

Forecast

Downscale

Forecast-Ensemble
20(12)th
09JST
1stinterval:15~member
03 JST-Grid
km 5 Sep.

15JST 5th

Results of nested LETKF system
6hour analysis window
observation data(every hour)03JST 5th
cda data (surface + upper)
LETKF
analysisboundary

Reflectionin outer
LETKF

downscalboundarye
Reflect
ion inouter
LETKF

Forecast

Downscale

Forecast-Ensemble
20(12)th
09JST
1stinterval:15~member
03 JST-Grid
km 5 Sep.

6hour analysis window
6hour analysis window
09JST
5th
15JST 5thobservation data(every hour)observation data(every hour)
LETKF
LETKF
cda data (surface + upper)
cda data (surface + upper)
analysisanalysis-Ensemble member :20(12)-grid interval 1.875 km
1 hour assimilation window observation data
(10minutes interval)
cda data (surface + upper),GPS etc.

Ensemble mean (15JST,15km)

1 hour assimilation window observation
data (10minutes interval)
cda data (surface + upper),GPS etc

Ensemble mean (15JST,2km)

・Detailed structures
were produced by
the inner LETKF
with a gridinterval of 2 km.

Results of nested LETKF system
6hour analysis window
observation data(every hour)03JST 5th
cda data (surface + upper)
LETKF
analysisboundary

Reflectionin outer
LETKF

downscalboundarye
Reflect
ion inouter
LETKF

Forecast

Downscale

Forecast-Ensemble
20(12)th
09JST
1stinterval:15~member
03 JST-Grid
km 5 Sep.

6hour analysis window
6hour analysis window
observation data(every hour)09JST 5th observation data(every hour) 15JST 5th
cda data (surface + upper)
cda data (surface + upper)
LETKF
LETKF
analysisanalysis-Ensemble member :20(12)-grid interval 1.875 km
1 hour assimilation window observation data
(10minutes interval)
cda data (surface + upper),GPS etc.

Ensemble mean (15JST,2km)

・Rainfall regions
were similar to theobserved ones.
(Rainfall regions
were produced overthe mountain areas)

1 hour assimilation window observation
data (10minutes interval)
cda data (surface + upper),GPS etc

Observation(15JST)Results of nested LETKF system
6hour analysis window
observation data(every hour)03JST 5th
cda data (surface + upper)
LETKF
analysisboundary

Reflectionin outer
LETKF

downscalboundarye
Reflect
ion inouter
LETKF

Forecast

Downscale

Forecast-Ensemble
20(12)th
09JST
1stinterval:15~member
03 JST-Grid
km 5 Sep.

6hour analysis window
6hour analysis window
observation data(every hour)09JST 5th observation data(every hour) 15JST 5th
cda data (surface + upper)
cda data (surface + upper)
LETKF
LETKF
analysisanalysis-Ensemble member :20(12)-grid interval 1.875 km
1 hour assimilation window observation data
(10minutes interval)
cda data (surface + upper),GPS etc.

Ensemble mean (15JST,2km)

・However, the rainfallthat developed into
the Sakai intense
rainfall was too small.

1 hour assimilation window observation
data (10minutes interval)
cda data (surface + upper),GPS etc

Observation(15JST)Results of nested LETKF system
6hour analysis window
observation data(every hour)03JST 5th
cda data (surface + upper)
LETKF
analysisboundary

Reflectionin outer
LETKF

downscalboundarye
Reflect
ion inouter
LETKF

Forecast

Downscale

Forecast-Ensemble
20(12)th
st ~member
09JST
1
03
JST
-Grid interval:15 km 5 Sep.

6hour analysis window
6hour analysis window
09JST
5th
observation data(every hour)observation data(every hour) 15JST 5th
cda data (surface + upper)
cda data (surface + upper)
LETKF
LETKF
analysisanalysis-Ensemble member :20(12)-grid interval 1.875 km
1 hour assimilation window observation data
(10minutes interval)
cda data (surface + upper),GPS etc.

Ensemble mean (15JST,2km)

・To be similar to theobserved distribution,higher resolution data,such as GPS PWV
and Doppler radar
data, are needed.

1 hour assimilation window observation
data (10minutes interval)
cda data (surface + upper),GPS etc

Observation(15JST)Low-level convergence of water vapor
・To improve convergence of water vapor, we wantto assimilate the precipitable water vapor (PWV)
and horizontal wind data .

・PWV can be obtained from delays of signals fromGNSS.

GNSS Satellite

GNSS
Satellite

ＧNSS Receiver
Moist airflow

Moist airHow to estimate PWV data
・A GPS network(GEONET) has been
established in Japan.
・Horizontal distances of
GPS receivers are about
25 km.
・GEONET provides highresolution data of PWV
that can express theenvironments of local
heavy rainfalls.

How to estimate PWV data
Assumption of ZTD:
linear distribution

ZTD

STD
STD
STD

ZTD

• Signals from GNSS satellites are delayed by water vapor in
the atmosphere. Delay along the path between GNSS
satellites and receivers is called the Slant Total Delay (STD).
• The Zenith Total Delay (ZTD) is estimated from the STDs
by multiplying the mapping function of the elevation angles.
• The Precipitable Water Vapor (PWV, vertically integrated
amount of water vapor) is estimated from the ZTDs.

Accuracy of GPS-PWV data
observed by GEONET
Comparison with GPS-PWV and sondePWV（Period Jun. 1999 ～ May 2000）.
Distances between GPS-PWV and sondePWV were less than 10km horizontallyand 20m vertically.

RMS = 2.3mm（Precise orbit was used in theestimation of GPS-PWV.)GEONET

Observed GPS-PWV

14JST

14JST

AnalysisAnalysisAnalysis
09JTS
15JST
03JTS
6hour analysis window
6hour analysis window
6hour analysis window

GPS-PWV were assimilated
from 9 to 15 JST very 10 minutes.

1hour analysis window
Obs. data (10minly)

B.C.

To Out LETKF

Forecast
・20member
・x=1.875km

B.C.

Downscale
To Out LETKF•Forecast•20members•x=15km

Downscale

Rainfall distribution at 14JST
on 5th Sep. and GPS-PWV andconvergence of water vapor.

14JST

1hour analysis window
Obs. data(10minly)
GPS data

Assimilation method GPS-PWV
Difference between the GPS
receivers’ altitude and model
topography <50 m.
First guess and statistical value
obtained by NHM-LETKF
Vertical profiles of T, RH and the
spread of RH within the range of±15 km from GPS receivers.

RH profiles were produced.
(Input data of LETKF)
Thinning of the verticallayer in the profiles was
performed, because of thevertical correlation of theobservation error.

Assumption: the difference between analysis
and first guess is larger at the layer with awider spread of RH.

Height(km)Observation data：GPS-derived PWVExample ofthe input data
Maximumof spreadInput
data of
LETKF

Water vapor was should be modifies only at thelayer of which the correlation with PWV waslarge.

±15km

Input RH data of the LETKF was produced byincreasing of average of first guess value inproportion to maximum spared of RH at layers,of which the correlation with PWV was large.

Average offirst guess
profiles.
RH(%)

GPS_PWV_2
17JST

CNTL 17JST

Assimilation of GPS-PWV data

GPS_PWV
17JST

CNTL 17JST

Assimilation of GPS-PWV data
The number of ensemble
forecasts in which Sakai
thunderstorm was
reproduced is increased.

GPS-slant water vapor (SWV) data
・Slant water vapor (SWV) was the water vapor amount along
the path from GPS satellite and GPS receivers
・ SWV was retrieved from PWV, gradient and residual, andsurface meteorological data, such as T and P.
・SWV was assimilated by using the same method of PWV,
except the paths were slanted.

±3 km

GPS_SWV
17JST

CNTL 17JST

Assimilation of GPS-SWV dataHorizontal wind obtained
from Doppler radars
1400JST

1428JST

1458JST

Osaka airport
convergence

Kansai
airport

Forecast
・20member
・x=1.875km

B.C.

1hour analysis window
Obs. data (10minly)

B.C.

To Out LETKF

Radial wind from14 -15JSTwere assimilated
every 10 minutes.

Downscale•Forecast•20members•x=15km

Downscale
To Out LETKF

Horizontal winds were estimated from the radialwinds of
AnalysisAnalysisAnalysis
09JTS
15JST
03JTS
Kansai and Osaka airports6hour analysis window 6hour analysis window 6hour analysis windowusing dual analysis.

1hour analysis window
Obs. data(10minly)

Doppler radar data

Radar_UV
17JST

CNTL 17JST

Horizontal wind improves the rainfall forecasts,
although the impact was weaker than GPS-PWV.

GPS_RadarUV
CNTL 17JST
17JST

Both GPS-PWV and horizontal wind wereassimilated simultaneously, number ofthe improved member were increased.

Blue and pink circles indicate the rainfalls improved by GPS and radar data

Radar_Vr
17JST

CNTL 17JST

Radial winds of Doppler radar also improve
the rainfall forecasts.

GPS_RadarVr
CNTL 17JST
17JST

Both GPS-PWV and radial wind wereassimilated simultaneously, the number ofthe improved members were increased.

Blue and pink circles indicate the rainfalls improved by GPS and radar data

Influence of nesting (Feedback to outer LETKF)
9月1日09時～5日03時

5日09時
6時間毎の解析
観測データ(1時間間隔)

LETKF
解析

15JST
5日15時

6時間毎の解析
観測データ(1時間間隔)

LETKF
解析

LETKF
解析

親
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デ
ル
へ
の
反
映

ダウ
ン
ス
ケー
親
ル
モ
デ
ル
へ
の
反
映

ダウ
ン
ス
ケー
ル

予報
・20メンバー・15km格子6時間毎の解析
観測データ(1時間間隔)

境界値

境界値
予報
・20メンバー・1.875km格子
1時間毎の解析
観測データ(10分間隔)

Spread (15JST,15km)

1時間毎の解析
観測データ(10分間隔)

Spread (15JST,15km)with nest

Ensemble mean (15JST, 15km)

0.1

15JST

予報
・20メンバー・15km格子w/o nest

Ensemble mean (15JST, 15km)

Spread (15JST, 15km)
予報
・20メンバー・1.875km格子

Spread (15JST,15km)
0.1

0.1

Spreads of with nest and w/o nest were compared.

Influence of nesting (Feedback to outer LETKF)
9月1日09時～5日03時

5日09時
6時間毎の解析
観測データ(1時間間隔)

6時間毎の解析
観測データ(1時間間隔)

LETKF
解析

LETKF
解析

親
モ
デ
ル
へ
の
反
映

ダウ
ン
ス
ケー
親
ル
モ
デ
ル
へ
の
反
映

ダウ
ン
ス
ケー
ル

予報
・20メンバー・15km格子

15JST
5日15時

6時間毎の解析
観測データ(1時間間隔)

LETKF
解析

境界値

境界値
予報
・20メンバー・1.875km格子with nest

1時間毎の解析
観測データ(10分間隔)

Spread (15JST,2km)

w/o nest

with nest
Spread (15JST,15km)

1時間毎の解析
観測データ(10分間隔)

Spread (15JST,15km)

Spread (15JST,15km)
0.1

0.1

予報
・20メンバー・15km格子w/o nest

Ensemble mean (15JST,2km)

Spread (15JST,2km)
予報
・20メンバー・1.875km格子

Spread (15JST,15km)
0.1

0.1

0.1

• Nesting increased the spread slightly in this case.
• Influence expanded into outer regions (pressure).
• More case studies are needed to obtain the conclusion.
0.1

Multi inner LETKFs experiment

Four inner LETKFs were deployed in the outer LETKF.
• Weight was determined from the distance from theboundary of Inner LETKF.
• Inner LETKFs are not necessarily deployed next toeach other. (example. deployed at each radar sites)

Overlap regions of patches (Boundary Problem )#001 15JST

Ensemble mean 15JST#004 17JST

Ensemble spread 15JST#004 17JST

Overlap regions of patches (Boundary Problem )#001 15JST

Ensemble mean 15JST

Ensemble spread 15JST• Four inner LETKFs were
deployed in the outer LETKF.
• Unrealistic convection cells
were not generated on theboundary.
#004 17JST

Further improvements
6hour analysis window
observation data(every hour)

6hour analysis window
observation data(every hour)

LETKF
analysis

1 hour assimilation window
observation data
(10minutes interval)

1：No-cost smoother
6hour analysis window
observation data(every hour)

3hour analysis window
observation data(every hour)
LETKF
analysisDownscale

Outer LETKF
Inner Forecast-Ensemble
member :20(12)member 20(12)-grid interval 1.875 km-Grid interval:15 km
Inner LETKF
member :20(12)-grid interval 1.875 km

2:Extraction frominner model outputs

6hour analysis window
observation data(every hour)no-cost smoother1 hour assimilation window
observation data
(10minutes interval)

3hour analysis window
observation data(every hour)
LETKF
analysis

LETKF
analysis

LETKF
analysis

Reflection in outer
LETKF

no-cost smoother

Downscale
Reflection in outer
LETKF

Downscale

Outer LETKF-Ensemble member 20(12)-Grid interval:15 km
Inner LETKF-Ensemble member :20(12)-grid interval 1.875 km

LETKF
analysis

3hour analysis window
observation data(every hour)
LETKF
analysis

Extraction

Extraction

Extraction

B.C.

B.C

B.C.

1 hour assimilation window1 hour assimilation window1 hour assimilation window
observation data
observation data
observation data
(10minutes
interval)(10minutes interval)(10minutes interval)

Utilization of no-cost smoother
6hour analysis window
03JST 5th 6hour analysis window 09JST 5th 6hour analysis window 15JST 5th
observation data(every hour)observation data(every hour)observation data(every hour)
LETKF
LETKF
LETKF
cda data (surface + upper)
cda data (surface + upper)
cda data (surface + upper)
analysisanalysisanalysis

1 hour assimilation window observation
data (10minutes interval)
cda data (surface + upper),GPS etc.

CNTL

#005 17JST 2km

Spread 17JST 2km

no-cost smootherboundary

Reflectionin outer
LETKF

boundary

downscale

Inner LETKF-Ensemble member :20(12)-grid interval 1.875 km

no-cost smoother

Reflectionin outer
LETKF

Downscale

Outer LETKF-Ensemble
20(12)
09JST
1st ~ member
03 JST
5th Sep.
-Grid interval:15 km

1 hour assimilation window observation
data (10minutes interval)
cda data (surface + upper),GPS etc

No-cost smoother#005 17JST 2km

Spread 17JST 2km

Rainfall region near the boundary was intensified.
Regions near the boundary were modified by no-cost smoother.

Influence of convective scale disturbances
03JST 5th

09JST 5th
LETKF
analysis

LETKF
analysis

No-cost smooth

No-cost smootherdownscaleReflection in
outer
LETKF

Downscale12)p.

boundaryLSL

1 hour assimilation window observation data
(10minutes interval)
cda data (surface + upper),GPS etc.

Lboundary

L S
1 hour assimilation window observation data
cda data (surface + upper),GP

Initiation CheckS
S

Outer LETKF

L  S

Inner LETKFSS  S  SSpline interpolation was used toestimate the mesoscale distribution

Influence of convective scale disturbances
L

S
S

Outer LETKF

L  S

Inner LETKFSS  S  SSpline interpolation was used toestimate the mesoscale distribution

Spread of 11JST (2 cycles of Inner LETKF, 2km)

L

S

Large spread area in
L corresponds tomountain ranges.
Sis different from L .
S should be usedbecause it contains
small scale
perturbations.

Further improvements
6hour analysis window
observation data(every hour)

6hour analysis window
observation data(every hour)

LETKF
analysis

1 hour assimilation window
observation data
(10minutes interval)

1：No-cost smoother
6hour analysis window
observation data(every hour)

3hour analysis window
observation data(every hour)
LETKF
analysisDownscale

Outer LETKF
Inner Forecast-Ensemble
member :20(12)member 20(12)-grid interval 1.875 km-Grid interval:15 km
Inner LETKF
member :20(12)-grid interval 1.875 km

2:Extraction frominner model outputs

6hour analysis window
observation data(every hour)no-cost smoother1 hour assimilation window
observation data
(10minutes interval)

3hour analysis window
observation data(every hour)
LETKF
analysis

LETKF
analysis

LETKF
analysis

Reflection in outer
LETKF

no-cost smoother

Downscale
Reflection in outer
LETKF

Downscale

Outer LETKF-Ensemble member 20(12)-Grid interval:15 km
Inner LETKF-Ensemble member :20(12)-grid interval 1.875 km

LETKF
analysis

3hour analysis window
observation data(every hour)
LETKF
analysis

Extraction

Extraction

Extraction

B.C.

B.C

B.C.

1 hour assimilation window1 hour assimilation window1 hour assimilation window
observation data
observation data
observation data
(10minutes
interval)(10minutes interval)(10minutes interval)

Summary and future plan
1. The nested LETKF system is under development
to reproduce the environments and convection
cells.
2. The Sakai intense rainfall on 5th Sep. 2008 was
reproduced by the nested LETKF system.
3. GPS-water vapor data and the radial winds ofthe radar data increased the number of forecasts
in which the thunderstorm was reproduced.
4. The number of kinds of assimilation data will be
increased. Other improvements on the nested
system will be implemented.

Tornado Occurred on 6th May 2012
住家の屋根はぎとられる

平沢
12:53

住家倒壊
北条

12:51
被害分布の幅：約500ｍ
12:49
山木
12:47

Rainfall region
that caused Tornado

12:45

テクノパーク大穂

12:43
吉沼

つくば市

常総市
12:41
12:39
大沢新田

12:37

新石下

12:35 非住家の屋根が東北東に約50m飛散

http://mainichi.jp

Tornado Occurred on 6th May 2012
3rd, 9JST

6時間毎の解析
観測データ(1時間間隔)

LETKF
解析

6時間毎の解析
観測データ(1時間間隔)

LETKF
解析

6Th 15, 21JST
6時間毎の解析 7th 9JSTLETKF

観測データ(1時間間隔)

境界値

予報
・20(12)メンバー
・1.875km格子

1時間毎の解析
観測データ(10分間隔）

解析

親モデルへ
の反映

ダウン

境界値

6th 9JST

スケール
親モデル
への反
映

ダウン
スケール

予報
・20(12)メンバー
・15km格子

6th, 3JST

1時間毎の解析
観測データ(10分間隔）

6th 10JST
Ensemble meanof Inner LETKF at 12JST
1 hour rainfall

Downscale experiments
Spread at 12JST
1 hour rainfallx=350 m

From 1130JST to 1430JST

#000

#001

#002

#003

#004

#005

#006

#007

#008

#009

#011

#012

Positions of vortices
more than 0.1(1/s)

10 members (83%)
duration: 6-36min

・Positions and durations depend on the ensemble members.
・Tornados occurred in three areas, which were the same as
the observations though they were shifted northward by 10 km.

350 m #004

350 m #004

350 m #007

350 m #007

From 1130JST
to 1430JST＃004：
Intense vortex ismaintained.
＃007:
Intense vortex
isn’t generated.

Average region
from 1230JST
to 1330JST
0.155

Duration (min)

0.15
0.145
0.14

36min
Qv (0.5km) 0.135
・Circles indicate
0.13
the duration of tornados.
0.125
・Duration is long when water 0.12
27
27.5
28
28.5
29
vapor at 550m height is large 26.5
UV(5km)-UV (0.5km)
and vertical shear between 5km-0.5km was large.

#004

・From 1240JST to 1320JST
・Shifted northward.
・Vortex was intensified at1250～1300JST.

・Red points indicate
vortices > 0.4 (1/s)
・Horizontal wind at 600 m
height・Rain at 20 m height.

#007

Downscale experiments x=50m

ΔP＝20P#004

Downscale experiments x=50m

ΔP＝1P

#007

Downscale experiments x=50m

ΔP＝12P#000

Downscale experiments x=50m

ΔP＝5P
ΔP＝2P

#001

Downscale experiments x=50m

ΔP＝8P

#002

Downscale experiments x=50m

ΔP＝9-7P#003

Downscale experiments x=50m

ΔP＝2P

#005

Thank you for your attention

Acknowledgements
GPS data were provided from the GSI.