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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