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