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GSI-based EnKF-Var hybrid data assimilation system: implementation and test for hurricane prediction Xuguang Wang, Xu Lu, Yongzuo Li, Ting Lei University.

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Presentation on theme: "GSI-based EnKF-Var hybrid data assimilation system: implementation and test for hurricane prediction Xuguang Wang, Xu Lu, Yongzuo Li, Ting Lei University."— Presentation transcript:

1 GSI-based EnKF-Var hybrid data assimilation system: implementation and test for hurricane prediction Xuguang Wang, Xu Lu, Yongzuo Li, Ting Lei University of Oklahoma, Norman, OK In collaboration with Mingjing Tong, Vijay Tallapragada, Dave Parrish, Daryl Kleist NCEP/EMC, College Park, MD Jeff Whitaker, Henry Winterbottom NOAA/ESRL, Boulder, CO 1

2 2 control forecast GSI-ACV Wang 2010, MWR control analysis data assimilation First guess forecast control forecast Ensemble covariance EnKF Whitaker et al. 2008, MWR EnKF analysis k member 1 forecast member 2 forecast member k forecast EnKF analysis 2 EnKF analysis 1 member 1 forecast member 2 forecast member k forecast member 1 analysis member 2 analysis member k analysis Re-center EnKF analysis ensemble to control analysis GSI-based Hybrid EnKF-Var DA system Wang, Parrish, Kleist, Whitaker 2013, MWR

3 3 GSI hybrid for GFS: GSI 3DVar vs. 3DEnsVar Hybrid vs. EnKF Wang, Parrish, Kleist and Whitaker, MWR, 2013  3DEnsVar Hybrid was better than 3DVar due to use of flow-dependent ensemble covariance  3DEnsVar was better than EnKF due to the use of tangent linear normal mode balance constraint

4 4 GSI-4DEnsVar: Naturally extended from and unified with GSI- based 3DEnsVar hybrid formula (Wang and Lei, 2014, MWR, in press). Add time dimension in 4DEnsVar GSI hybrid for GFS: 3DEnsVar vs. 4DEnsVar

5 5 Wang, X. and T. Lei, 2014: GSI-based four dimensional ensemble-variational (4DEnsVar) data assimilation: formulation and single resolution experiments with real data for NCEP Global Forecast System. Mon. Wea. Rev., in press. GSI hybrid for GFS: 3DEnsVar vs. 4DEnsVar Results from Single Reso. Experiments  4DEnsVar improved general global forecasts  4DEnsVar improved the balance of the analysis  Performance of 4DEnsVar degraded if less frequent ensemble perturbations used  4DEnsVar approximates nonlinear propagation better with more frequent ensemble perturbations  TLNMC improved global forecasts

6 6 GSI hybrid for GFS: 3DEnsVar vs. 4DEnsVar 16 named storms in Atlantic and Pacific basins during 2010

7 7 Approximation to nonlinear propagation –3h increment propagated by model integration 4DEnsVar (hrly pert.) 4DEnsVar (2hrly pert.) 3DEnsVar -3h 0 3h * time Hurricane Daniel 2010

8 3DEnsVar outperforms GSI3DVar. 4DEnsVar is more accurate than 3DEnsVar after the 1-day forecast lead time. Negative impact if using less number of time levels of ensemble perturbations. Negative impact of TLNMC on TC track forecasts. 8 Verification of hurricane track forecasts

9 9 Development and research of GSI based Var/EnKF/hybrid for regional modeling system GSI-based Var/EnKF/3D- 4DHybrid GFS Hurricane- WRF (HWRF) WRF ARW WRF-NMMB Poster: Johnson et al. “Development and Research of GSI based Var/EnKF/hybrid Data Assimilation for Convective Scale Weather Forecast over CONUS.”

10 10 GSI hybrid for HWRF Hurricane Sandy, Oct  Complicated evolution  Tremendous size  147 direct deaths across Atlantic Basin  US damage $50 billion New York State before and after nhc.noaa.gov

11 11 Sandy 2012 Model: HWRF Observations: radial velocity from Tail Doppler Radar (TDR) onboard NOAA P3 aircraft Initial and LBC ensemble: GFS global hybrid DA system Ensemble size: 40 Experiment Design

12 12 Model: HWRF Observations: radial velocity from Tail Doppler Radar (TDR) onboard NOAA P3 aircraft Initial and LBC ensemble: GFS global hybrid DA system Ensemble size: 40 Experiment Design Oper. HWRF

13 13 TDR data distribution (mission 1) P3 Mission 1

14 14 Verification against SFMR wind speed Last Leg

15 15 Comparison with HRD radar wind analysis

16 16 Comparison with HRD radar wind analysis SN

17 17 Track forecast (RMSE for 7 missions)

18 18 Experiments for seasons Case# Correlation between HRD radar wind analysis and analyses from various DA methods

19 19 ISSAC 2012 (mission 7)

20 Verification against SFMR and flight level data

21 21 Experiments for season Track MSLP

22 22 Two-way Dual Resolution Hybrid for HWRF 3km movable nest ingests 9km HWRF EnKF ensemble Two-way coupling Tests with IRENE 2011 assimilating airborne radar data 9km 3km

23 Two-way Dual resolution hybrid

24 Summary and ongoing work 24 GFS a.GSI-based 4DEnsVar for GFS improved global forecast and TC forecast. b.The analysis generated by 4DEnsVar was more balanced than 3DEnsVar. c.the performance of 4DEnsVar was in general degraded when less frequent ensemble perturbations were used. d.The tangent linear normal mode constraint had positive impact for global forecast but negative impact for TC track forecasts. e.Preliminary tests showed positive impact of the temporal localization on the performance of 4DEnsVar. HWRF a.The GSI-based hybrid EnKF-Var data assimilation system was expanded to HWRF. b.Various diagnostics and verifications suggested this unified GSI hybrid DA system provided more skillful TC analysis and forecasts than GSI 3DVar and than HWRF GSI hybrid ingesting GFS ensemble. c.Airborne radar data improved TC structure analysis and forecast, TC track and intensity forecasts. Impact of the data depends on DA methods. d.Dual-resolution (3km-9km) two way hybrid for HWRF showed promising results. e.Developing/enhancing 4DEnsVar hybrid and assimilation of other airborne data and other data from NCEP operational data stream for HWRF.

25 Outer Domain – assimilate operational conventional surface and mesonet observations, RAOB, wind profiler, ACARS, and satellite derived winds every 3 hours to define synoptic/mesoscale environment 12 km 25 Johnson, Wang et al Development and Research of GSI-based Var/EnKF/hybrid DA for Convective Scale Weather Forecasts over CONUS Inner Domain – assimilate velocity and reflectivity from NEXRAD radar network every 5 min during last 3hr cycle Poster: Johnson, Wang, Lei, Carley, Wicker, Yussouf, Karstens 4 km

26 Precipitation forecast skill averaged over 10 complex, convectively active cases GSI-EnKF forecasts are more skillful than GSI-3DVar forecasts for all thresholds and lead times. Benefits of radar data are more pronounced assimilated by GSI-EnKF than GSI-3DVar. 26

27 May 8 th 2003 OKC Tornadic Supercell Ref and vorticity at 1 km 27 W and Vort. at 4 km Lei, Wang et al hr forecast from 22Z GSI hybrid

28 28 DA cycling configuration (mission 1) 0000Z28 Cold Start 1800Z25 Spin-up Forecast 0200Z26 Deterministic Forecast DA Cycle 2200Z25 OBS GSI3DVar Spin-up Forecast 0000Z28 Deterministic Forecast OBS Hybrid 1800Z25 Ensemble Spin-up Forecast 0000Z Z26 Deterministic Forecast DA Cycle 2200Z25 OBS HWRF EnKF Ensemble Perturbation

29 29 DA cycling configuration (mission 1) Spin-up Forecast 0000Z28 Deterministic Forecast OBS Ensemble Perturbation 0200Z Z25 GFS ENS Hybrid-GFSENS

30 30 GSI-3DEnsVar: Extended control variable (ECV) method implemented within GSI variational minimization (Wang 2010, MWR): Extra term associated with extended control variable Extra increment associated with ensemble (4D)EnKF: ensemble square root filter interfaced with GSI observation operator (Whitaker et al. 2008) GSI-based Hybrid EnKF-Var DA system


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