Something about DOTSTAR (Dropsonde Observations for Typhoon Surveillance near the Taiwan Region)

Chun-Chieh Wu Department of Atmospheric Sciences

National Taiwan University

Collaborators: Po-Hsiung Lin, Jan-Huey Chen, K.-S. Chou (NTU), T.-C. Yeh (CWB), Sim Aberson (HRD), T. Nakazawa (JMA/MRI), Dave Parson (NCAR),

Seon Park (Ewha Womans Univ.), Sharan Majumdar (U. of Miami), Melinda Peng, and C. Reynolds (NRL)

(Wu et al. 2005a, b)

• Overview of DOTSTAR

• Real-time forecast/analysis application

• Impact evaluation

• Targeted observations

• Future Prospects

Flow chart of DOTSTARAstra jet of AIDC

69

JMA,UKMO,

….

(Wu et al. 2005a, BAMS)

69

DOTSTAR missions （ 2003 to 2006）Up to present, 24 missions have been conducted in DOTSTAR for 20 typhoons, with 386 dropsondes deployed during the 129 flight hours.

8 typhoons affecting mainland China

4 typhoons affecting Japan

2 typhoons affecting Korea

23. Saomai22. Bopha21. Kaemi20. Bilis 24. Shanshan

Real-time DOTSTAR data in CWB’s WINSMeari, 1200 UTC, 25 September, 2004

• Dropsonde sounding data

• Flight-level wind and sfc. wind

9.78.558.2

14.111.511.0

18.117.316.3

15.314.513.3

12.814.312.2

17.715.014.5

14.013.713.0

18.917.918.1

22.421.820.0

30.327.425.8

24.720.320.4

15.819.914.0

23.924.622.2

15.018.515.7

13.614.613.2

Dropsonde

MBL

WML150

Unit: m/s

Real-time surface wind analysisBilis ( 碧利斯 ) : A weak typhoon (Vmax = 25 m/s), yet with very large and

moisture-laden outer circulation

NCEP GFS : 14%

JMA GSM : 19%

NOGAPS : 14%

ENSEMBLE : 22%

The impact on global models in 2004

(Wu et al. 2006a, WF)

Sim

Aberson

Tetsuo

Nakazawa

Melinda Peng

Impact to mesoscale models:

Combination of the Dropwindsonde data and the bogused vortex

(Chou and Wu 2006)

Targeted observation in DOTSTAR • Adaptive observations : observations targeted in sensitive regions

can reduce the initial condition’s uncertainties, and thus decrease forecast error.

• Factors associated with targeted observations - Magnitude of uncertainty - Growth of uncertainty - Data assimilation system• Targeted observation is an active research topic in NWP, with plans plans

for field programsfor field programs, tests of new observing systemstests of new observing systems, and application of new concepts in predictability and data assimilationpredictability and data assimilation. (Langland 2005)

• In DOTSTAR, due to limited aircraft resources, targeted observing strategies for these missions must be developed.– NOGAPS Singular Vector (collaborating with Reynolds)– NCEP/GFS ETKF (collaborating with Majumdar)– NCEP/GFS DLM variance (collaborating with Aberson)– MM5 adjoint sensitivity (ADSSV)MM5 adjoint sensitivity (ADSSV)(Wu et al. 2005, BAMS; 2006, JAS)

• Verifying area ： A box is centered on the forecast location of typhoon at the verifying time.

• Response function ： Define the average wind field within the verifying area at the verifying time.

hPa300

hPa850 A

hPa300

hPa850 A1

dxdydp

udxdydpR

hPa300

hPa850 A

hPa300

hPa850 A2

dxdydp

vdxdydpR

0 h 6h 12 h 18h 24 h 36h

-36h -24 h -18h - 12 h -6h - 0 h

MM5 forecast model

MM5 MM5 adjointadjoint modelmodel

Observing time

Verifying time

Xin Xoutm

MT

0 h 6h 12 h 18h 24 h 36h

-36h -24 h -18h - 12 h -6h - 0 h

MM5 forecast model

MM5 MM5 adjointadjoint modelmodel

Observing time

Verifying time

Xin Xoutm

MT

• The forward and backward integrations of the adjoint modeling system ：

• Adjoint-Derived Sensitivity Steering Vector (ADSSVADSSV)

– A unique new definition to identify the sensitive (and targeted observing) areas to the steering flow at the verifying time.

21 R,

RADSSV w.r.t. vorticity ：

MagnitudeMagnitude – the degree ofdegree of sensitivitysensitivityDirectionDirection – the change of the steering flow direction steering flow direction w.r.t. the vorticity variation.

(Wu et al. 2006c, JAS)

• Higher sensitivity to the northeast of Typhoon Mindulle

• More impact on the meridional movement

• Typhoon Mindulle (2004)

Results

0629_00Z

0627_12Z

MM5

(Wu et al. 2006c, JAS)

ADSSV w.r.t. vorticity ：

21 R,

R

Targeted observations in DOTSTARDLM Variances, Toth and Kalnay (1993)

ETKF, Bishop and Majumdar (2001)

FNMOC SV, Palmer et al. (1998) ADSSV, Wu et al. (2005)

Operation of ADSSV• DOTSTAR(Wu et al. 2006c)•G-IV surveillance (Etherton et al. 2006)

Session Rapporteur of the IWTC VI meeting, Nov. 21-30, 2006

Singular Vector,

JMA, Yamaguchi

How the dropsonde data improve the forecast? How the dropsonde data improve the forecast? Typhoon Conson (2004) as an exampleTyphoon Conson (2004) as an example

Typhoon Conson (2004) 8 June 1200UTC

(Nakazawa 2004, THORPEX meting)

JMA-GSM

Evaluate a SV method as a strategy for Targeting ObservationJMA has executed Observing System Experiments (OSEs) to investigate the usefulness of the singular vector method as a strategy for sensitive analysis.

For the initial time of 12UTC 08 June 2004 when totally 16 dropsondes were dropped into typhoon CONSON by the DOTSTAR (Dropsonde Observation for Typhoon Surveillance near the Taiwan Region) project, 4 predictions with JMA Global Spectral Model (TL319L40) about the use of the dropsondes in the global 4D-Var analysis are executed.

(I) all dropsonde observations are used for making the initial condition

(II) dropsondes are not used at all

(III) only 3 data within a sensitive region are used (4, 9, 12)

(IV) only data outside of a sensitive region are used (6, 8, 10, 13, 15, 16)

The distribution means vertically accumulated total energy by the 1st moist singular vector.

Targeted area for the SV calculation is N25-N30, E120-E130.

Optimization time interval is 24 hours.

Sensitive analysis result

x

CONSON’s center position

(From Yamaguchi)

OSEs result on CONSON’s track forecast

(III) (I) (IV)

(II) is almost same with (IV)similar

Red: (I) all dropsonde observations are used for making the initial condition

Blue: (II) dropsondes are not used at all

Green: (III) only 3 data within a sensitive region are used (4, 9, 12)

Water: (IV) only data outside of a sensitive region are used (6, 8, 10, 13, 15, 16)

(From Yamaguchi)

Exp. Dropsonde data DA scheme Others

CTL None X

3DVAR All 3DVAR

3DVAR-N10 Northern 10 drops 3DVAR

3DVAR-S6 Southern 6 drops 3DVAR

3DVAR-1000850 1000-850 hPa 3DVAR

3DVAR-700400 700-400 hPa 3DVAR

3DVAR-300200 300-200 hPa 3DVAR

3DVAR-850300 850-300 hPa 3DVAR

CRSSMN All Cressman

CTL-nTW None X No Taiwan

3DVAR-nTW All 3DVAR No Taiwan

CTL-BG None X bogused

3DVAR-BG All 3DVAR bogused

Scientific objectives: To evaluate the impact of different subsets of the dropwindsonde data, the data assimilation schemes, the presence of Taiwan terrain, and the bogusing scheme to the typhoon track simulation.

A

B

C

D

E

Impact StudyImpact Study

(Wu et al. 2006b)Impact from Wind vs. mass

Future prospects

• Data – no data can stand alone

• Models – dynamics and physics

• Data assimilation and targeted observation

• Collaborating with CWB, NSC, and Typhoon and Flood Research Center, …

• International joint program – THORPEX/PARC, HRD, NRL, ONR…

• Typhoon reconnaissance

THORPEX-PARC Experiments and Collaborating Efforts (from Dave Parsons)

NRL P-3 and NRL P-3 and HIAPER with theHIAPER with theDLR Wind LidarDLR Wind Lidar

NRL P-3 and NRL P-3 and HIAPER with theHIAPER with theDLR Wind LidarDLR Wind Lidar

Upgraded Russian Upgraded Russian Radiosonde Network for IPYRadiosonde Network for IPY

Winter storms Winter storms reconnaissancereconnaissanceand driftsondeand driftsonde

JAMSTEC/IORGGJAMSTEC/IORGG