1. Assimilation of T-TREC-retrieved wind data with WRF 3DVAR for the short-Term forecasting of Typhoon Meranti (2010) at landfall Xin Li 1, Yuan Wang 1, Jie Ming 1, Kun Zhao 1, Ming Xue 2 1 The Key Laboratory of Mesoscale Severe Weather, School of Atmospheric Sciences, Nanjing University, China School of Atmospheric Sciences, Nanjing University, China 2 Center for Analysis and Prediction of Storms and School of Meteorology, University of Oklahoma Meteorology, University of Oklahoma

2. 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).

3. 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.

4. T1 T2 T-TREC wind vector Searching distance Initial cell Target cell T-TREC Retrieving wind T-TREC VS. TREC R 1) Polar coordinates centered on the TC center 2)Anti-clock wise searching 3)Velocity correlation matrix 4)Objective center finding and searching area determining

5. T-TREC TREC Saomai(0608) Z=1km 1hour before landfall Wang and Zhao, 2010

6. Meranti(2010)

7. Radar data information and coverage Vr T-TREC 3-km wind

8. Experiment CTL WRF 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 ExpVr ExpTrec Vr T-TREC

9. Model Grid CTLExpVrExpTrec Domain3 nested 257*237 12km 462*462 4km 615*615 1.33km 3 nested 257*237 12km 462*462 4km 615*615 1.33km 3 nested 257*237 12km 462*462 4km 615*615 1.33km ObservationNoneRadial velocity (Vr)T-TREC wind Assimilation window NoneOnly once at initial time PhysicsLin microphysics YSU boundary-layer Kain-Fritsch (Domain 1) Lin microphysics YSU boundary-layer Kain-Fritsch (Domain 1) Lin microphysics YSU boundary-layer Kain-Fritsch (Domain 1)

10. Radar data impact at initial time CTL ExpVr ExpTrec

11. Impact on Typhoon structure Forecast 06h 12h 18h CTL ExpVr ExpTrec OBS D03 1.33km

12. Impact on Track and Intensity Forecast D03 1.33km

13. Impact on 6-h accumulated Precipitation Forecast 06-12h 12-18h CTL ExpVr ExpTrec OBS D03 1.33km

14. 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

15. 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

16. 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?

17. The hybrid formulation…. Ensemble covariance is implemented into the 3D-VAR cost function via extended control variables: 3D-VAR increment Total increment including hybrid Weighting coefficient for static 3D-VAR covariance 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

18. Hybrid data assimilation Be matrix : Ensemble flow-dependent & 3DVAR static 0600 UTC/09 1200 UTC/09 0600UTC/10 Deterministic Forecast -6h0h18h Initial Ensemble Forecast Hybrid DA T-TREC wind 30 members Generate Ensemble perturbations use RANDOMCV in WRF-3DVAR

19. Spread of 6-h pre-ensemble forecast 3-km V-wind Ens-Mean 3-km V-wind Ens-Spread

20. Exp3DVARExpHybrid0.5ExpHybrid1.0 Domain3 nested 257*237 12km 462*462 4km 615*615 1.33km 3 nested 257*237 12km 462*462 4km 615*615 1.33km 3 nested 257*237 12km 462*462 4km 615*615 1.33km ObservationT-TREC wind Assimilation window 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

21. Flow-dependent B matrix impact 3-km V-wind Single point Test 3DVARHybrid0.5Hybrid1.0

22. Empirical Vertical Covariance Localization Apply Gaussian Vertical Covariance Localization function: Old: Grid-Dependent Localization Scale New: Distance-Dependent Localization Scale L : 10 grids K : vertical grids L : 3000 m K : vertical distance Spurious sampling error are not only confined to horizontal error correlations, it affects vertical too. So vertical localization is needed.

23. Impact of Vertical Covariance Localization No vertical localization With new vertical localization With old vertical localization 3-km wind Single point TestVertical cross-section increment

24. Flow-dependent B matrix impact 3-km Wind analysis and increment by T-TREC wind 3DVARHybrid0.5Hybrid1.0

25. Flow-dependent B matrix impact 1-km T increment Vertical cross-section 3DVARHybrid0.5Hybrid1.0

26. Impact on Track and Intensity Forecast D03 1.33km

27. Exp3DVARExpHybrid0.5ExpHybrid1.0OBS Impact on Typhoon structure Forecast 06h 12h 18h D03 1.33km

28. Summary The 3DVAR performs well in vortex circulation initialization while the mass fields are adjusted during the models 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.

29. Thanks