Improving High Resolution Tropical Cyclone Prediction Using a Unified GSI-based Hybrid Ensemble-Variational Data Assimilation System for HWRF Xuguang Wang University of Oklahoma, Norman, OK In close collaboration with NCEP/EMC, ESRL/PSD, AOML/HRD 1 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 2 10 years

33 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 GFS HWRF Rapid Refresh (RR) NAM

44 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.

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

6 6 control forecast GSI-ECV control analysis data assimilation First guess forecast control forecast Ensemble covariance EnKF 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 ensemble-variational DA system Wang, Parrish, Kleist, Whitaker 2013, MWR

77 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 GSI-based hybrid ensemble-variational DA system EnKF: square root filter interfaced with GSI observation operator (Whitaker et al. 2008)

88 Why Hybrid? “Best of both worlds” VAR (3D, 4D) EnKFHybridReferences (e.g.) Benefit from use of flow dependent ensemble covariance instead of static B Yes Hamill and Snyder 2000; Lorenc 2003; Etherton and Bishop 2004; Zupanski 2005, Wang et al. 2007ab,2008ab, 2009; Buehner et al. 2010ab; Wang 2011, etc. Robust for small ensemble or large model error Yes Wang et al. 2007b, 2009; Buehner et al. 2010b Better localization for integrated measure, e.g. satellite radiance; radar with attenuation Yes Campbell et al Flexible to add various dynamical/physical constraints yesYes Wang et al Use of various existing capabilities in VAR (e.g., Outer loops to treat nonlinearity; Variational QC) yesYes Summarized in Wang 2010, MWR

9 Experiment with Airborne radar Model: HWRF Δx=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 Initial test focuses on the impact of the use of the ensemble covariance in GSI IRENE 2011 Li et al. 2013, AMS presentation

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

11 TDR data distribution

12 TDR data distribution m/s

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

Verification against independent flight level wind speed km GSI 3DVar3DEnsVar First Leg 14

Verification against SFMR wind speed km GSI 3DVar3DEnsVar First Leg 15

Verification against independent flight level wind speed km GSI 3DVar3DEnsVar Last Leg 16

Verification against SFMR wind speed km 3DEnsVarGSI 3DVar Last Leg 17

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

19 Comparison with radar wind analysis m/s GSI 1kmHRD radar wind 1km 19

Comparison with radar wind analysis m/s GSI 3kmHRD radar wind 3km 20

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 21 3DEnsVar 3DVar

MSLP forecast 22 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 3DEnsVar 3DVar

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

24 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 adjoint of 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 GSI based 4DENSVAR

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