Assimilation of T-TREC-retrieved wind data with WRF 3DVAR for the short-Term forecasting of Typhoon...

Assimilation of T-TREC-retrieved wind data with WRF Assimilation of T-TREC-retrieved wind data with WRF 3DVAR for the short-Term forecasting of Typhoon 3DVAR for the short-Term forecasting of Typhoon

Meranti (2010) at landfallMeranti (2010) at landfall

Xin LiXin Li11, Yuan Wang, Yuan Wang11, Jie Ming, Jie Ming11, Kun Zhao, Kun Zhao11, Ming Xue, Ming Xue22

11The Key Laboratory of Mesoscale Severe Weather,The Key Laboratory of Mesoscale Severe Weather, School of Atmospheric Sciences, Nanjing University, ChinaSchool of Atmospheric Sciences, Nanjing University, China

22Center for Analysis and Prediction of Storms and School of Center for Analysis and Prediction of Storms and School of Meteorology, University of OklahomaMeteorology, University of Oklahoma

Background• Doppler radar is the only platform that observes

the 3D structure of Typhoons at high enough temporal and spatial resolutions.

• Significant progress has been made in the TC forecasting using Radar data direct assimilation (Vr and Reflectivity).

• Wind field is crucial in Typhoon assimilation and the importance of full coverage by Multi-Doppler Radar and cycling assimilation. (Xiao et al. 2005,2007; Zhao and Jin 2008; Zhang et al. 2009,2011; Zhao and Xue,2009,2011,2012).

Motivation

• Single Radar provides full information of wind field in Typhoon inner core.

• T-TREC (an extended TREC retrieving method) uses the information of both Reflectivity and Vr to retrieve wind field. Make full use of the large coverage of Reflectivity data.

• Provide full circle of vortex circulation in the inner-core region.

T1 T2

T-TREC wind vectorT-TREC wind vector

Searching distanceSearching distance

Initial cellInitial cell

Target cellTarget cell

T-TREC Retrieving wind （ T-TREC VS. TREC）

RVZ

1) Polar coordinates centered on the TC center2)Anti-clock wise searching3)Velocity correlation matrix4)Objective center finding and searching area determining

T-TRECT-TREC

TRECTREC

Saomai(0608) Z=1km 1hour before landfallWang and Zhao, 2010

Meranti(2010)

Radar data information and coverage

VrVr T-TRECT-TREC

3-km wind

Experiment

CTLWRF Forecast from GFS Reanalysis

1200 UTC/09 1800 UTC/09 0000 UTC 0600UTC/10

WRF Forecast with Radar Vr DA

WRF Forecast with Radar T-TREC wind DA

1200 UTC/09 1800 UTC/09 0000 UTC 0600UTC/10

1200 UTC/09 1800 UTC/09 0000 UTC 0600UTC/10

ExpVr

ExpTrec

Vr

T-TREC

Model Grid

CTL ExpVr ExpTrec

Domain 3 nested257*237 12km462*462 4km615*615 1.33km

3 nested257*237 12km462*462 4km615*615 1.33km

3 nested257*237 12km462*462 4km615*615 1.33km

Observation None Radial velocity (Vr) T-TREC wind

Assimilation window

None Only once at initial time

Only once at initial time

Physics Lin microphysicsYSU boundary-layerKain-Fritsch (Domain 1)

Lin microphysicsYSU boundary-layerKain-Fritsch (Domain 1)

Lin microphysicsYSU boundary-layerKain-Fritsch (Domain 1)

Radar data impact at initial time

CTLCTL ExpVrExpVr ExpTrecExpTrec

Impact on Typhoon structure Forecast

06h

12h

18h

CTLCTL ExpVrExpVr ExpTrecExpTrecOBSOBSD03 1.33km

Impact on Track and Intensity Forecast

D03 1.33km

Impact on 6-h accumulated Precipitation Forecast

06-12h

12-18h

CTLCTL ExpVrExpVr ExpTrecExpTrecOBSOBS

D03 1.33km

Conclusion

• The impact of T-TREC retrieving wind has been recognized in Typhoon forecast at landfall

• The assimilation only need once due to the large coverage and full vortex circulation of T-TREC retrieving data

• The improved Typhoon initial condition by T-TREC wind data leads to not only the better track, intensity and structure prediction, but also the precipitation forecast even no Reflectivity data is assimilated

Recent research

• The climatological (static) background error covariance matrix (B matrix) of 3DVAR only reflect the constraint of large scale balance and the flow-dependent covariance through ensemble is needed.

• The ensemble-based flow dependent background error covariance matrix could reflect the current flow pattern and correct multivariate covariance for Typhoon structure

• WRF Hybrid En-3DVAR assimilation system(Wang et al.,2007,2008,2011) incorporates ensemble flow dependent background covariance in the 3DVAR by extending the control variables in variational framework, combining climatological and flow-dependent background error covariance

WRF Hybrid En-3DVAR

• Flow-dependent B matrix is important and can be adapted to the existing 3D-VAR system easily through an extended control variable

• The physics constraint could be added easily to the variational framework of Hybrid En-3DVAR

• Hybrid can be robust for small size ensembles.• While, similar with EnKF, the horizontal and

vertical covariance localization are applied.

WHY Hybrid? Advantage?

The hybrid formulation….Ensemble covariance is implemented into the 3D-VAR cost function via extended control variables:

J(x1' , ) 1

1

2x1

'TB 1x1' 2

1

2 TC 1

1

2(yo ' Hx ' )T R 1(yo ' Hx ' )

x ' x1' ( k oxk

e )k1

K

3D-VAR incrementx1'

x' Total increment including hybrid

1 Weighting coefficient for static 3D-VAR covariance

2 Weighting coefficient for ensemble covariance Extended control variable

C: correlation matrix for ensemble covariance localization

(Wang et. al. 2008)

Conserving total variance requires:

β1+β2=1

Hybrid data assimilation

Be matrix : Ensemble flow-dependent & 3DVAR static

0600 UTC/09 1200 UTC/09 0600UTC/10

Deterministic Forecast

-6h 0h 18h

Initial Ensemble Forecast

Hybrid DAT-TREC wind

30 members

Generate Ensemble perturbations use“RANDOMCV” in

WRF-3DVAR

Spread of 6-h pre-ensemble forecast

3-km V-wind Ens-Mean 3-km V-wind Ens-Spread

Exp3DVAR ExpHybrid0.5 ExpHybrid1.0

Domain 3 nested257*237 12km462*462 4km615*615 1.33km

3 nested257*237 12km462*462 4km615*615 1.33km

3 nested257*237 12km462*462 4km615*615 1.33km

Observation T-TREC wind T-TREC wind T-TREC wind

Assimilation window

Only once at 1200 UTC/09

Only once at 1200 UTC/09

Only once at 1200 UTC/09

Background error covariance matrix

Only 3DVAR static(β1=1.0,β2=0)

Hybrid 3DVAR static and Ensemble flow-dependent (β1=0.5,β2=0.5)

Only Ensemble flow-dependent (β1=0,β2=1.0)

Experiment configuration

Flow-dependent B matrix impact

3-km V-wind Single point Test

3DVAR Hybrid0.5 Hybrid1.0

Empirical Vertical Covariance LocalizationApply Gaussian Vertical Covariance Localization function:

(k kc ) exp k kc 2/ Lc

2

Old:Grid-Dependent Localization Scale

New:Distance-Dependent Localization Scale

L : 10 gridsK : vertical grids

L : 3000 mK : vertical distance

Spurious sampling error are not only confined to horizontal error correlations, it affects vertical too.So vertical localization is needed.

Impact of Vertical Covariance Localization

No vertical localization

With new vertical localization

With old vertical localization

3-km wind Single point Test Vertical cross-section increment

Flow-dependent B matrix impact

3-km Wind analysis and increment by T-TREC wind

3DVAR Hybrid0.5 Hybrid1.0

Flow-dependent B matrix impact

1-km T increment

Vertical cross-section

3DVAR Hybrid0.5 Hybrid1.0

Impact on Track and Intensity Forecast

D03 1.33km

Exp3DVAR ExpHybrid0.5 ExpHybrid1.0OBS

Impact on Typhoon structure Forecast

06h

12h

18h

D03 1.33km

Summary• The 3DVAR performs well in vortex circulation initialization

while the mass fields are adjusted during the model’s spinning up mostly

• The Hybrid En-3DVAR provides more balance analysis due the use of flow-dependent B matrix even it only from the cold start pre-ensemble forecast. The enhanced thermal structure leads to better intensity and structure prediction

• Based on three Typhoon case (chanthu,megi,2010,not shown). Ensemble-based flow-dependent B matrix is important for Typhoon structure assimilation.

• The cycling use of T-TREC wind or the so-called Multi-scale assimilation (T-TREC combining Vr) are being tested ongoing for more balanced initial condition.

Thanks