1. Development and Testing of a Regional GSI-Based EnKF-Hybrid System for the Rapid Refresh Configuration Yujie Pan 1, Kefeng Zhu 1, Ming Xue 1,2, Xuguang Wang 1,2, Jeffrey S. Whitaker 3, Stanley G. Benjamin 3 and Stephen S. Weygandt 3 and Ming Hu 3 Center for Analysis and Prediction of Storms 1 and School of Meteorology 2 University of Oklahoma, Norman Oklahoma 73072 NOAA Earth System Research Laboratory 3, Boulder, Colorado 5 th EnKF Workshop Albany, New York May 2012 1

2. 2 Outline Part 1: Introduction to the regional GSI-based EnKF- hybrid data assimilation system Part 2: Single observation tests Part 3: Comparison of hybrid with GSI and pure EnKF  EnKF-Hybrid 1 way interactive  EnKF-Hybrid 1 way with multi-physics EnKF  EnKF-Hybrid 2 way interactive  Verification of precipitation forecasts on 13 km grid

3. 3 Extended control variable method (Lorenc 2003) in 3D GSI hybrid (Wang 2010, MWR): Extra term associated with extended control variable Extra increment associated with ensemble GSI-Hybrid: Method

4. EnKF EnKF—RR RUC EnKF Domain 207x207 grid points ~40 km, 51 levels Precip. Forecast Domain 532x532 grid points ~13 km, 51 levels Precip. Verification Domain RUC Domain as indicated Ensemble members 40 Experiment Domains 4

5. Observations assimilated Sounding and profiler Surface data from land stations and ships Aircraft Satellite retrieve winds 5

6. Single Observation Tests (Comparing GSI, Hybrid and EnKF) 6 3DVAR Different weight for the static covariance in Hybrid Solid line: Height at 600 hPa (background) Shading: Temperature increment EnKF Weight=1 Weight=0 Weight=0.5 Hybrid Half static Half flow-dependent

7. 7 Hybrid GSI-EnKF DA system: 1 way coupling control forecast Hybrid control analysis control forecast data assimilation First guess forecast EnKF analysis k member 1 forecast member 2 forecast member k forecast EnKF EnKF analysis 2 EnKF analysis 1 member 1 forecast member 2 forecast member k forecast Ensemble covariance …… Wrf-DFL 0 20m 40m Wrf-DFL 0 20m 40m Wrf-DFL 0 20m 40m GSI observations Innovation EnKF Hybrid

8. 8 ExperimentsHorizontal localization (KM) Vertical localization (ln(p)) Fix inflationAdaptive inflation EnKF1000 KM (height dependent) 1.1/1.6 (height dependent) 0.10.9 Hybrid 1way1000 KM1.1 …… Time (UTC) 3hr fcst 00 03 obs Obs 12 3hr fcst Background Fields Background Fields EnKF & hybrid Analysis Fields EnKF & hybrid 2010-05-08 00:00 obs 21 3hr fcst EnKF & hybrid 2010-05-17 21:00 ………… Interpolation 13 KM 12 hr Fcst Interpolation 13 KM 12 hr Fcst Hybrid And EnKF Configuration

9. Surface Variables Verification (RMSE; 3-18 hr Forecasts) 9 Hybrid 1way EnKF GSI 3dvar 18h 3h 3-18 hour forecasts verification against surface data.

10. 10 Verifications Against Soundings (RMSE) Hybrid 1way EnKF GSI-3dvar

11. 11 Verifications Against Soundings (RMSE)

12. groupLong waveShort waveSurface layerPBLCumulus p1rrtm scheme (1)Goddard short wave (2) Monin-Obukhov (Janjic) scheme (2) Mellor-Yamada- Janjic TKE scheme (2) Grell 3D ensemble scheme (5) p2rrtm scheme (1)Dudhia scheme (1)Monin-Obukhov scheme (1) YSU scheme (1)Kain-Fritsch (new Eta) scheme (1) p3rrtm scheme (1)Goddard short wave (2) MYNN surface layer (5) MYNN 2.5 level TKE scheme (5) Grell-Devenyi ensemble scheme (3) p4GFDL (Eta) longwave (99) GFDL (Eta) short wave (99) Monin-Obukhov (Janjic) scheme (2) Mellor-Yamada- Janjic TKE scheme (2) Grell 3D ensemble scheme (5) p5rrtm scheme (1)Goddard short wave (2) Monin-Obukhov (Janjic) scheme (2) Mellor-Yamada- Janjic TKE scheme (2) Grell-Devenyi ensemble scheme (3) 12 Multi-physics GSI-EnKF Hybrid System Configuration

13. Surface variables verification (RMSE; 3-18 hr Forecasts) 13 When Multiple-physics schemes were employed for EnKF, hybrid was also improved. Multi-hybrid Single-hybrid GSI 3dvar

14. Verifications Against Soundings (RMSE) 14 Multi-hybrid Single-hybrid GSI 3dvar

15. Sensitivity Tests To Covariance Weight 15 ExperimentWeight to static covariance Hybrid 000.0 Hybrid 010.1 Hybrid050.5 Hybrid 090.9 GSI 3dvar1.0 Hybrid main parameters: Horizontal localization : ~1100 KM Vertical localization : 1.1 ( ln(p) ) Verifications Against Soundings 1100 KM horizontal localization improve the performance of hybrid at jet level

16. Hybrid GSI-EnKF DA system: 2 way coupling 16 member 1 forecast member 2 forecast member k forecast control forecast GSI-ECV EnKF control analysis EnKF analysis k EnKF analysis 2 EnKF analysis 1 member 1 analysis member 2 analysis member k analysis member 1 forecast member 2 forecast data assimilation control forecast Ensemble covariance Re-center EnSR analysis ensemble to control analysis Re-center EnSR analysis ensemble to control analysis …… First guess forecast GSI observations Innovation member k forecast Wrf-DFL 0 20m 40m Wrf-DFL 0 20m 40m Wrf-DFL 0 20m 40m Wrf-DFL 0 20m 40m Wang et al. 2011

17. 17 Surface Variables Verification (RMSE) Hybrid 2way EnKF GSI-3dvar Single-physics EnKF was used.

18. 18 Verifications Against Soundings (RMSE) Hybrid 2way EnKF GSI-3dvar

19. 19 Verifications Against Soundings (RMSE)

20. 20 OBS (NCEP Stage IV)GSIEnKF 2010051111 2010051305 11 hr forecast started from 2010051100 5 hr forecast started from 2010051300 Hybrid2way Hourly Precipitation Forecasts on 13 km Grid

21. 21 Verification of Hourly QPF on 13 km Grid Hybrid 2way EnKF GSI

22. Conclusions The GSI-based hybrid (run at 40 km grid spacing for RAP data set and model), with either 1-way or 2-way interaction with a single-physics EnKF and using equal weight for static and flow-dependent covariances, outperforms the GSI and pure EnKF for most verified variables (relative humidity, temperature, wind), except surface temperature. The advantage lasts up to the 18 hour forecast time. The hybrid with half static covariance is better than the one without static covariance, indicating the benefit of including static covariance for the current application. EnKF and hybrid predict more accurate precipitation pattern and location on a 13 km grid than GSI, which is also demonstrated by ETS score. But hybrid doesn’t improve the precipitation forecasts as much as EnKF. The performance of the EnKF system is noticeably improved when multiple physics schemes are used in the ensemble forecast, especially for temperature and moisture fields. 22

23. Future Plan (in collaboration with GSD and EMC) Use height-dependent localization for flow-dependent covariance in the hybrid – found helpful within EnKF Use well tuned multi-physics EnKF within 2-way hybrid. Test the impact of the strong constraint available in GSI Add satellite data. Implement and test dual-resolution (40/13 km) hybrid Test the system with hourly cycles Eventual quasi-operational testing of hourly cycled, two-way interactive EnKF/hybrid system for RAP including radar data. Long term: Hybrid system applied to NARRE (North America Rapid Refresh Ensemble) and HRRRE (High-Resolution Rapid Refresh Ensemble) Nesting CAPS’s Storm-Scale EnKF within (see Youngsun Jung’s talk) 23

24. Thank you!! 24

25. state-dependent covariance inflation Fix inflation Adaptive inflation Final inflation taper(r)

26. Pressure (hPa) Vertical smoothing-scale (vz) in GSI Step1: vz*( log( p(k-1)/psf )-log( p(k+1)/psf) )/2 Step2: vz=vz/1.5 Vertical smoothing scales in GSI p(k): average pressure at the k-th model level psf: average surface pressure Convert to vertical grid units loc = loc*coefficent