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Xuguang Wang University of Oklahoma, Norman, OK

Improving High R esolution T ropical C yclone Prediction U sing a Unified GSI-based Hybrid Ensemble- V ariational D ata A ssimilation S ystem for HWRF. Xuguang Wang University of Oklahoma, Norman, OK In close collaboration with NCEP/EMC, ESRL/PSD, AOML/HRD.

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Xuguang Wang University of Oklahoma, Norman, OK

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  1. Improving High Resolution Tropical Cyclone Prediction Using a Unified GSI-based Hybrid Ensemble-VariationalData Assimilation System for HWRF Xuguang Wang University of Oklahoma, Norman, OK In close collaboration with NCEP/EMC, ESRL/PSD, AOML/HRD Warn On Forecast and High Impact Weather Workshop, Feb. 6, 2013

  2. Background History of hybrid DA Development of theory Research with simple model and simulated data System development for real NWP model and test real data Operational implementation at NWP centers for global model: e.g., GSI-based hybrid Ensemble-Variational DA for NCEP GFS 10 years 2

  3. Background GSI is a unified system which provides data assimilation for current operational global and regional forecast system. Efforts are being conducted to integrate the same GSI-based hybrid DA system with operational regional forecast systems. GSI-EnKF hybrid HWRF GFS NAM Rapid Refresh (RR) 3

  4. Background Unifying GSI-based hybrid DA system with operational regional systems facilitates faster transition to operations. The focus of the project is the extension, application, extensive testing and research of the GSI-based hybrid data assimilation for the HWRF modeling system at high resolutions. Also motivated by encouraging results of ensemble based data assimilation for tropical cyclones. 4

  5. Background 3DVAR hybrid • Hurricane IKE 2008 • WRF ARW: Δx=5km • Observations: radial velocity from two WSR88D radars (KHGX, KLCH) • WRFVAR hybrid DA system (Wang et al. 2008a) 700 mb wind 700 mb wind 850 mb temp 850 mb temp Liet al., 2012, MWR 5

  6. EnKF analysis 1 EnKF analysis 2 EnKF analysis k control analysis control forecast GSI-based hybrid ensemble-variational DA system Re-center EnKF analysis ensemble to control analysis member 1 forecast member 1 analysis member 1 forecast EnKF member 2 forecast member 2 analysis member 2 forecast Ensemble covariance member k forecast member k analysis member k forecast control forecast GSI-ECV First guess forecast data assimilation 6 Wang, Parrish, Kleist, Whitaker 2013, MWR

  7. GSI-based hybrid ensemble-variational DA system • GSI-ECV: Extended control variable (ECV) method (Lorenz 2003; Buehner 2005; Wang et al. 2007a, 2008a, Wang 2010): Extra term associated with extended control variable Extra increment associated with ensemble • EnKF: square root filter interfaced with GSI observation operator (Whitaker et al. 2008) 7

  8. Why Hybrid?“Best of both worlds” Summarized in Wang 2010, MWR 8

  9. Experimentwith Airborne radar • Model: HWRFΔx=9km ( 3km next) • Observations: radial velocity from Tail Doppler Radar (TDR) • Case: IRENE 2011 • Initial and LBC ensemble: GFS global hybrid DA system • Ensemble size: 40 • Initialtest focuses on the impact of the use of the ensemble covariance in GSI IRENE 2011 IRENE 2011 Li et al. 2013, AMS presentation

  10. DA cycling configuration GSI 3DVar Hybrid (1 way coupling) EnKF

  11. TDR data distribution

  12. TDR data distribution m/s

  13. 700 mb wind increment 3DEnsVar GSI 3DVar m/s m/s

  14. Verification against independent flight level wind speed First Leg GSI 3DVar 3DEnsVar km km

  15. Verification against SFMR wind speed First Leg GSI 3DVar 3DEnsVar km km

  16. Verification against independent flight level wind speed Last Leg GSI 3DVar 3DEnsVar km km

  17. Verification against SFMR wind speed Last Leg GSI 3DVar 3DEnsVar km km

  18. Comparison with radar wind analysis GSI 3DVAR HRD radar wind analysis 3DEnsVar km km km km m/s m/s m/s 18 18

  19. Comparison with radar wind analysis HRD radar wind analysis @ 1km GSI 3DVar @ 1km 3DEnsVar @ 1km m/s m/s m/s 19 19

  20. Comparison with radar wind analysis HRD radar wind analysis @ 3km GSI 3DVar @ 3km 3DEnsVar @ 3km m/s m/s m/s

  21. Track forecast EMC: HWRF official forecast NoDA: no TDR assimilation GSI 3DVar: assimilating TDR using GSI 3DVar EnKF: assimilating TDR using EnKF 3DEnsVar: assimilating TDR using 3DEnsVar 3DVar 3DEnsVar

  22. MSLP forecast 3DVar 3DEnsVar EMC: HWRF official forecast NoDA: no TDR assimilation GSI 3DVar: assimilating TDR using GSI 3DVar EnKF: assimilating TDR using EnKF 3DEnsVar: assimilating TDR using 3DEnsVar

  23. Summary and ongoing work The GSI-based hybrid EnKF-Var data assimilation system was expanded to assimilate TDR data for HWRF. TDR data showed positive impact on TC track and intensity forecasts and verification against independent observations. Various diagnostics and verifications suggested ensemble-based data assimilation (hybrid, EnKF) provided more skillful TC analysis and forecasts than the GSI 3DVar. Testing more missions/cases. Testing dual resolution 3km/9km hybrid; 3km’s own hybrid. Developing and testing GSI-based hybrid EnKF-Varwith moving nests. Add and test more observations. Develop and research on various new capabilities for HWRF hybrid (4DEnsVar, hydrometeor, post balance constraint, etc.).

  24. GSI based 4DENSVAR • The current GSI hybrid is 3D (3DEnsVar); temporal evolution of error covariance with the assimilation window not considered • Observations (e.g., satellite, radar) are spreading through the DA window. • 4DENSVAR is further developed. • Different from traditional 4DVAR, 4DENSVAR conveniently avoid the tangent linear and adjointof the forecast model which is challenging to build and maintain (e.g., Buehner et al. 2010). • Much cheaper compared to traditional 4DVAR being developed for GSI (Rancic et al. 2012). 24

  25. 4DENSVAR performance: Test with GFS at reduced resolution 2010 hurricane track forecast Anomaly correlation for height Lei, Wang, Kleist, Whitaker, 2013 25

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