1 / 30

Xuguang Wang, Xu Lu, Yongzuo Li, Ting Lei University of Oklahoma, Norman, OK

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

cate
Download Presentation

Xuguang Wang, Xu Lu, Yongzuo Li, Ting Lei University of Oklahoma, Norman, OK

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. GSI-based EnKF-Var hybrid data assimilation system: implementation and test for hurricane prediction Xuguang Wang, Xu Lu, YongzuoLi, 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

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

  3. GSI hybrid for GFS: GSI 3DVar vs. 3DEnsVar Hybrid vs. EnKF • 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 Wang, Parrish, Kleist and Whitaker, MWR, 2013

  4. GSI hybrid for GFS: 3DEnsVar vs. 4DEnsVar • 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

  5. 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 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.

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

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

  8. Verification of hurricane track forecasts • 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.

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

  10. GSI hybrid for HWRF Hurricane Sandy, Oct. 2012 • Complicated evolution • Tremendous size • 147 direct deaths across Atlantic Basin • US damage $50 billion New York State before and after nhc.noaa.gov

  11. Experiment Design • 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 Sandy 2012

  12. Experiment Design • 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 Oper. HWRF

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

  14. Verification against SFMR wind speed Last Leg

  15. Comparison with HRD radar wind analysis

  16. Comparison with HRD radar wind analysis S N

  17. Track forecast (RMSE for 7 missions)

  18. Experiments for 2012-2013 seasons Correlation between HRD radar wind analysis and analyses from various DA methods Case#

  19. ISSAC 2012 (mission 7)

  20. Verification against SFMR and flight level data

  21. Experiments for 2012-2013 season MSLP Track

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

  23. Two-way Dual resolution hybrid

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

  25. Development and Research of GSI-based Var/EnKF/hybrid DA for Convective Scale Weather Forecasts over CONUS Poster: Johnson, Wang, Lei, Carley, Wicker, Yussouf, Karstens • 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 4 km • Inner Domain • assimilate velocity and reflectivity from NEXRAD radar network every 5 min during last 3hr cycle 12 km Johnson, Wang et al. 2014

  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.

  27. May 8th 2003 OKC Tornadic Supercell 1hr forecast from 22Z GSI hybrid GSI hybrid Ref and vorticity at 1 km W and Vort. at 4 km Lei, Wang et al. 2014

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

  29. DA cycling configuration (mission 1) OBS Hybrid-GFSENS Spin-up Forecast Deterministic Forecast 0000Z28 Ensemble Perturbation GFS ENS 0200Z26 2200Z25

  30. GSI-based Hybrid EnKF-Var DA system • (4D)EnKF: ensemble square root filter interfaced with GSI observation operator (Whitaker et al. 2008) • 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 30

More Related