1GSI-based EnKF-Var hybrid data assimilation system: implementation and test for hurricane prediction Xuguang Wang, Xu Lu, Yongzuo Li, Ting LeiUniversity of Oklahoma, Norman, OKIn collaboration withMingjing Tong , Vijay Tallapragada, Dave Parrish, Daryl KleistNCEP/EMC, College Park, MDJeff Whitaker, Henry WinterbottomNOAA/ESRL, Boulder, CO
2GSI-based Hybrid EnKF-Var DA system Wang, Parrish, Kleist, Whitaker 2013, MWRmember 1 forecastmember 2 forecastmember k forecastEnKF analysis 2EnKF analysis 1member 1 analysismember 2 analysismember k analysisRe-center EnKF analysis ensembleto control analysismember 1 forecastmember 2 forecastmember k forecastEnKF Whitaker et al. 2008, MWREnsemble covarianceEnKF analysis kcontrol forecastcontrol analysiscontrol forecastGSI-ACV Wang 2010, MWRFirst guess forecastdata assimilation
3GSI 3DVar vs. 3DEnsVar Hybrid vs. EnKF GSI hybrid for GFS:GSI 3DVar vs. 3DEnsVar Hybrid vs. EnKF3DEnsVar Hybrid was better than 3DVar due to use of flow-dependent ensemble covariance3DEnsVar was better than EnKF due to the use of tangent linear normal mode balance constraintWang, Parrish, Kleist and Whitaker, MWR, 2013
4GSI 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
5Results from Single Reso. Experiments GSI hybrid for GFS:3DEnsVar vs. 4DEnsVarResults from Single Reso. Experiments4DEnsVar improved general global forecasts4DEnsVar improved the balance of the analysisPerformance of 4DEnsVar degraded if less frequent ensemble perturbations used4DEnsVar approximates nonlinear propagation better with more frequent ensemble perturbationsTLNMC improved global forecastsWang, 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.
6GSI 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 integration4DEnsVar (hrly pert.)4DEnsVar (2hrly pert.)3DEnsVarHurricane Daniel 2010*time-3h h
8Verification 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.
9GSI-based Var/EnKF/3D-4DHybrid Development and research of GSI based Var/EnKF/hybrid for regional modeling systemGSI-based Var/EnKF/3D-4DHybridGFSHurricane-WRF (HWRF)WRF ARWWRF-NMMBPoster: Johnson et al. “Development and Research of GSI based Var/EnKF/hybrid Data Assimilation for Convective Scale Weather Forecast over CONUS.”
10GSI hybrid for HWRF Hurricane Sandy, Oct. 2012 Complicated evolution Tremendous size147 direct deaths across Atlantic BasinUS damage $50 billionNew York State before and afternhc.noaa.gov
11Experiment Design Model: HWRF Observations: radial velocity from Tail Doppler Radar (TDR) onboard NOAA P3 aircraftInitial and LBC ensemble: GFS global hybrid DA systemEnsemble size: 40Sandy 2012
12Experiment Design Model: HWRF Observations: radial velocity from Tail Doppler Radar (TDR) onboard NOAA P3 aircraftInitial and LBC ensemble: GFS global hybrid DA systemEnsemble size: 40Oper.HWRF
24Summary and ongoing work GFSGSI-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.HWRFThe 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 hybrid and assimilation of other airborne data and other data from NCEP operational data stream for HWRF.
25Development and Research of GSI-based Var/EnKF/hybrid DA for Convective Scale Weather Forecasts over CONUSPoster: Johnson, Wang, Lei, Carley, Wicker, Yussouf, KarstensOuter Domainassimilate operational conventional surface and mesonet observations, RAOB, wind profiler, ACARS, and satellite derived winds every 3 hours to define synoptic/mesoscale environment4 kmInner Domainassimilate velocity and reflectivity from NEXRAD radar network every 5 min during last 3hr cycle12 kmJohnson, Wang et al. 2014
26Precipitation 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.
27May 8th 2003 OKC Tornadic Supercell 1hr forecast from 22ZGSI hybridGSI hybridRef and vorticity at 1 kmW and Vort. at 4 kmLei, Wang et al. 2014
30GSI-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 variableExtra increment associated with ensemble30