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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

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  1. Development and Testing of a Regional GSI-Based EnKF-Hybrid System for the Rapid Refresh Configuration Yujie Pan1, Kefeng Zhu1, Ming Xue1,2, Xuguang Wang1,2, Jeffrey S. Whitaker3, Stanley G. Benjamin3 and Stephen S.Weygandt3 and Ming Hu3 Center for Analysis and Prediction of Storms1 and School of Meteorology2University of Oklahoma, Norman Oklahoma 73072 NOAA Earth System Research Laboratory3, Boulder, Colorado 5thEnKF Workshop Albany, New York May 2012

  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. GSI-Hybrid: Method 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

  4. Experiment Domains 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 EnKF EnKF—RR RUC

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

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

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

  8. Hybrid And EnKF Configuration 13 KM 12 hrFcst 13 KM 12 hrFcst Interpolation Interpolation EnKF & hybrid EnKF & hybrid EnKF & hybrid EnKF & hybrid ………… …… Background Fields Analysis Fields Time (UTC) 21 3hr fcst 03 00 12 3hr fcst 3hr fcst 3hr fcst obs obs obs Obs 2010-05-08 00:00 2010-05-17 21:00

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

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

  11. Verifications Against Soundings (RMSE)

  12. Multi-physics GSI-EnKFHybrid System Configuration

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

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

  15. Sensitivity Tests To Covariance Weight Verifications Against Soundings 1100 KM horizontal localization improve the performance of hybrid at jet level Hybrid main parameters: Horizontal localization : ~1100 KM Vertical localization : 1.1 ( ln(p) )

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

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

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

  19. Verifications Against Soundings (RMSE)

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

  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.

  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)

  24. Thank you!!

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

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

  27. loc = loc*coefficent

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