30 November 2009 - 2 December International Workshop on Advancement of Typhoon Track Forecast...

30 November 2009 - 2 December

International Workshop on Advancement of Typhoon Track Forecast Technique 　 11

Observing system experiments using the operational NWP system of JMA

with T-PARC 2008 special observations targeted for Typhoon Sinlaku

Koji Yamashita*1, Yoichiro Ohta*1, Kiyotomi Sato*1 and Tetsuo Nakazawa*2

1 : Japan Meteorological Agency

2 : Meteorological Research Institute



Outline• Objectives

• Global Experiments Specification

• Results of OSEs– Mean Track Forecast Errors– Mean Central Pressure Errors– Case study at 12UTC 11th September

• Verification of sensitivity analysis system– Two case studies

• Trial of OSEs using RS-MTSAT-2-AMV

• Summary

2



Objectives

Observational dataEvaluation

• To investigate effectiveness of next generation forecast technology, "Interactive forecast system“

• To evaluate targeted observational data for 　typhoon track forecasts using the JMA 　　operational NWP system Observatio

n　System

Sensitivity　Analysis

Numerical　Prediction

Assimilation



Global Experiments Specification• Model

– Global Spectral Model ： TL959L60 ( 20km )(Reduced Gaussian Grid; top 0.1hPa)

• Assimilation

– 4D-Var method• Forecasts

– 84 hours for statistical evaluation and longer hours for case studies

• Target Typhoon ( September, 2008 )

– SINLAKU

• From 00UTC 09/09/2008 to 18UTC 18/09/2008

– JANGMI

• From 00UTC 25/09/2008 to 18UTC 30/09/2008Presentation of Mr. Ohta



Experimental Design• TEST – Special Observations are assimilated ( With Drop ).

• CNTL – No Special Observations are assimilated ( Without Drop ).

• BOGUS - TC 　 bogus data are assimilated (with BOGUS : instead of special observation)

Utilization of special observations TEST CNTL BOGUS

Dropsonde ○ (use) × ×

Special upper sounding(3-hourly)

○ × ×

TC BOGUS × （ no use) × ○



Typhoon Track and Special Observations Distribution Map

●： Dropsonde　▲： Ship 　 ★：Observatory

Before recurvature

After recurvature

Blue ： Before recurvature （ to 06UTC 14th Sep. ）Green ： After recurvature （ from 00UTC 16th Sep. ）



Results of OSEs (TEST vs CNTL / BOGUS vs CNTL)

– Mean Track Forecast Errors – Mean Central Pressure Errors– Case study at 12UTC 11th September



0

100

200

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400

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0 12 24 36 48 60 72 84Forecast time（hours）

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tional

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）0

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Results of OSEs 　 : TEST 　　　　　　　　　　　　　　～ Mean Track

Forecast Error for SINLAKU ～before-recurvature stage after-recurvature stagesignificant improvement (95% conf. lev.)

10% Improvement for 66- to 84-hour forecasts

23-30% Improvementfor 12-h forecasts

0

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0 12 24 36 48 60 72 840

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データ数

TESTCNTL

Number of data

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0 12 24 36 48 60 72 840

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データ数

TESTCNTL



Results of OSEs 　 : BOGUS ～ Mean Track Forecast Error for SINLAKU ～

before-recurvature stage after-recurvature stage

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tional

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tional

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Number of data

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0 12 24 36 48 60 72 840

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BOGUSCNTL

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0 12 24 36 48 60 72 840

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0 12 24 36 48 60 72 840

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BOGUSCNTL

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0 12 24 36 48 60 72 840

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10-23% Worse for 6- to 30-h forecasts

15% Improvementfor 60- to 72-h forecasts

No significant difference



Results of OSEs 　 : TEST ～ Mean Central Pressure Error for SINLAKU ～

before-recurvature stage after-recurvature stage

- 10

0

10

20

30

40

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0 12 24 36 48 60 72 84

Forecast time（hours）

ME o

f C

entr

al P

ress

ure

err

or

（hPa）

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Count

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0 12 24 36 48 60 72 840

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TESTCNTL

- 10

0

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0 6 12182430364248546066727884


ME o

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entr

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ress

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err

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（hPa）

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0 12 24 36 48 60 72 840

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TESTCNTL

Number of data

significant improvement (95% conf. lev.)

Over 10hPa reduction of intensity bias for 36-hour forecasts

Little significant difference



Results of OSEs 　 : BOGUS ～ Mean Central Pressure Error for

SINLAKU ～before-recurvature stage after-recurvature stage

- 10

0

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20

30

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0 12 24 36 48 60 72 84


ME o

f C

entr

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ress

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err

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（hPa）

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- 10

- 8

- 6

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0 6 12182430364248546066727884


ME o

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（hPa）

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significant improvement (95% conf. lev.)

0

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0 12 24 36 48 60 72 840

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BOGUSCNTL

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0 12 24 36 48 60 72 840

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0 12 24 36 48 60 72 840

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BOGUSCNTL

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0 12 24 36 48 60 72 840

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Number of data

Over 10hPa reduction of intensity bias for 24-hour forecasts

Slightly stronger intensity bias 　 for 18- to 84-h forecasts



Case study at 12UTC 11th September12

Core

SINLAKU environmental field

11Z

13Z12Z

14Z

C-130 targeted observation data and observed time

TC center

Case study using C-130 aircraft data

Typhoon Forecast Track

○： using observations × ： using no observations

C-130 Others Drop BOGUS

With Drop ○ ○ ×

Without C-130 × ○ ×

With BOGUS × × ○

12

1314

15 1617

18

1213

14

16 17

18

19

2019

15 Rapid recurvature

and fast moving

Test (with Drop): Increased track forecast errors by using all C-130 data

Without C-130 (almost same as CNTL): Better track forecasts at the beginning With BOGUS: Better track forecasts at the

beginning. Recurvature didn’t occur.



Why was the worse typhoon track forecast brought for “With Drop”?

(m)

850-1000 hPa

Difference of average analysis (Z and Wind) at 12UTC 11th September

400-700 hPa

m/s

Test ( With Drop ) – Cntl ( Without C-130 )

Deepened Z and strengthened circulation in the south or east side of TC center

Moving the typhoon to the south, and next moving to the north-east TC track forecast of

Test from FT0 to FT12

FT0

FT6

FT12



What brought the worse typhoon track forecast ?

14

1000hPa

700hPa900hPa

S

N

EW

300km

200km

Dropsonde average wind observations around the center of SINLAKU at 12UTC 11th September 2008 　

Assimilated dropsonde observations in the SINLAKU core fields within 200km

Different from observed typhoon center

Biased and warpedvortex to the south

Moving the typhoon to the south Next moving to the north-east

Probably the worse forecast is caused by assimilating the dropsonde data in the core.



First Summary

• Special observations contributed to improve the track and intensity forecasts especially during the before-recurvature stage.

• The typhoon track errors increased by using special observations .

General results for SINLAKU

From case Study at 12UTC 11th September

• It's better not to assimilate dropsonde observations in vicinity of the TC center.

• TC bogus data instead of special observation were generally efficient to improve the TC track and intensity forecasts.



Outline• Objectives

• Global Experiments Specification

• Results of OSEs– Mean Track Forecast Errors– Mean Central Pressure Errors– Case study at 12UTC 11th September

• Verification of sensitivity analysis system– Two case studies

• Trial of OSEs using RS-MTSAT-2-AMV

• Summary

16



Verification of sensitivity analysis system

First case study of 9/11 from 00 to 12 UTC



0

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0 6 12 18 24 30 36 42 48 54 60 66 72 78 84

Forecast time (hour)

Posi

tional

err

or

(km

)

CNTLCase ACase B

TEST with special observations – 9/11 from 00 to 12 UTC －

High sensitive area

Forecasted field T+24h

Case ACase B

Init. 00 UTC 10/09/2008 Sensitivity area

using SV method at 00 UTC 11/09/2008

(First SV)

Case A : Using special observations in the N-E area of typhoon center

Case B : Using special observations in the S-W area of typhoon center

CNTL : No special observations are assimilated

Case A : decreased

Case B ： increased 　

Track forecast errors

Mean Track Forecast Error



TEST with special observations of Case A for humidity – 9/11 from 00 to 12 UTC －



(First SV)

Targeted area

EXP. Wind Temperature humidity

ALL ○ ○ ○NOVAPOR ○ ○ ×

CNTL × × ×

ALL : av. 16% Improvement

NOVAPOR : av. 10% Improvement

Case A =

ALL : av. 32 - 45% Improvement



Why the errors were reduced in Case A ?

N N

E E

Wind Wind

Q ：N

Q ：NE

Q ：E

Q ：N

Q ：NE

Q ：E

The mean departure of 3D dropsonde wind and mixing ratio observations

from the first-guess (O-B) from the analysis (O-A)

Red vector : greater than 10 m/s Blue vector : greater than 5 m/s

The analysis was close to the observations.

The special observations in the north-east sensitivity area of TC center contributed to

reduce the track errors.



Why the errors increased in Case B?

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0 6 12 18 24 30 36 42 48 54 60 66 72 78 84


Posi

tional

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CNTLCase ACase B

Mean Track Forecast Error

High sensitive area

Forecasted field T+24h

Case ACase B

Worse forecasts



Why the errors increased in Case B?

Case A

Case BCNTL （no use) O-A in Case B

O-B in Case BN

N

E

E

W

W

blue 5m/s≧　　red 10m/s≧

TC track forecast

BST: Observed TC position at JMA

The observations were not reflected in the analysis..

The departure of 3D dropsonde wind

The number of observations in Case B was much less than in Case A.

We are considering the reason.



Second SummaryVerification of sensitivity analysis system

• Case study of 11th September– Special observations in high sensitive area in the N-E

quadrant of SINLAKU environmental field were effective.

– Possibility as the tool of “ Interactive forecast system”

• The improvement of TC track forecasts was found in the early hour and after FT=60.

• The humidity observations contributed to improve TC track forecast errors after FT=60. However, they brought almost no impact of forecasts in the early hour.

( All OBS : av. 16% Improvement , without humidity: av. 10% Improvement )



Second SummaryVerification of sensitivity analysis system

• Case study of 11th September – Special observations in high sensitive area in the S-W side of

SINLAKU environmental field brought worse TC track forecasts.

• The observations were not fitting to the analysis.• The number of observations was much less than in the N-E side of

TC center.• We are considering the reason..



Verification of sensitivity analysis system

Second case study of 00 UTC 10th September



TEST with special observations – 9/10 00 UTC －

Area A

Area B

Special Observations



(First SV) 0

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0 12 24 36 48 60 72 84


Pos

itio

nal er

ror

(km

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NODATAALL(A+B)Area AArea B

Exp. SP.OBS: Area A

SP.OBS: Area B

Result of forecasts

NODATA × × CNTL

ALL(A+B) ○ ○ Neutral

Area A ○ × Worse

Area B × ○ Better



Cause of result in case of 00 UTC 10 September

Area A

NO DATA

Area BALL(A+B)

TC track forecast

BST: Observed TC position at JMA

Modification of circulation in the N-E area of TC center ( Area A )

O-B in ALL

O-A in ALL

blue 5m/s≧　　red 10m/s≧

Moving the typhoon to the northward especially in Area A

N

E

W

N

E

W

S

SBetter

Better



Third SummaryVerification of sensitivity analysis system• Case study of 10th September

– Special observations in high sensitive area in the N-E side of TC were not effective.

– Special observations in low sensitive area in the S-W side of TC were effective.

• Modifying of 　 counterclockwise circulation in the N-E area of TC center, and moving the typhoon to the northward



Trial of OSEs using RS-MTSAT-2-AMV

at 18 UTC 17th September



Experimental Design ( TEST and CNTL)• Same as global experiments specification for special

observations– TL959L60 ( 20km ), 4D-Var method

• Without the TC bogus data and special observations

• Usage of two kinds of MTSAT-2 rapid scan AMV ( only TEST )– Cloud images of the intervals of 4 or 7 minutes

• All winds are thinned by 1°in horizontal and 100 hPa in vertical. A minimum horizontal distance is 100km.

• Only one wind selected per box in the hourly time window • Added to other AMVs of satellite data

– The intervals of 15 minutes (same as usage of the others GS wind ) • All winds are thinned by 2°in horizontal and 100 hPa in vertical. A minimum

horizontal distance is 200km.• Only one wind selected per box in the 6 hour time window • Other AMVs are assimilated together.



Example 　 of AMV after QC at 18 ～ 19UTC 11th September

≒100 km thinning

Others 200 km thinning

Red color 　≦ 400hPa level

Blue color 　≧ 850hPa level



Result of OSE

Slightly improvement of slow bias speed for TC track forecasts

TC track forecast

18

19

20

19

Mean Positional Error For SINLAKU

（00 UTC 9 September 2008 ～18 UTC 14 September 2008）

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TESTCNTL

Mean Positional Error For SINLAKU

（00 UTC 9 September 2008 ～18 UTC 14 September 2008）

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tiona

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（km

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TESTCNTL

Track Forecast Error

Possibility of improvement for TC track forecasts using RS-MTSAT-2-AMV data



Thank you for your attention

33


International Workshop on Advancement of Typhoon Track Forecast Technique 　

TC bogus ( Outline )35

• Tropical Cyclone generated on the Northwest Pacific ocean

• Wind speed is greater than 28 knots.

• Setup• Horizontal

• Psea • Wind except TC center • Four bogus data are located every 200 km from

TC center. • Vertical

• Wind except TC center• Located on the standard isobaric surface

(1000,925,850, 700,500,400 and 300 hPa )



Case study at 12UTC 11th September36

Core


11Z

13Z12Z

14Z


TC center

11Z 12Z 13Z 14Z Result

With Drop ○ ○ ○ ○ Worse (Rapid recurvature and moving)

Without C-130 × × × × Better track forecast at the beginning

With 11/12UTC ○ ○ × × Better; Recurvature did not occur

With 13UTC × × ○ × Better track forecast at the beginning; Nearly BST

With 14UTC × × × ○ Better track forecast at the beginning

Without 13UTC ○ ○ × ○ Nearly BST ( good track forecast )

With 13/14UTC × × ○ ○ Nearly BST ( good track forecast )

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141312

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1213

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19 2017

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141312

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Case study using C-130 aircraft data

Typhoon Forecast Track

Increased track forecast errors by using all C-130 data

Better track forecasts at the beginning

○： using observations × ： using no observations

Sensitive in the TC center core fields



Core


11Z

13Z12Z

14Z14Z


TC 　center 　

30 November 2009 - 2 December International Workshop on Advancement of Typhoon Track Forecast...

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