Ming Hu 1,2 , Yujie Pan 3 , Kefeng Zhu 3 , Xuguang Wang 3 , Ming Xue 3 ,

1. 17th conference on IOAS-AOLS Austin, TX 8 January 2013 Development of an EnKF/Hybrid Data Assimilation System for MesoscaleApplication with the Rapid Refresh Ming Hu1,2, Yujie Pan3, Kefeng Zhu3, Xuguang Wang3, Ming Xue3, David Dowell1, Steve Weygandt1, Stan Benjamin1, Jeff Whitaker4, Curtis Alexander1,2 1. Global System Division, ESRL/NOAA, Boulder, CO 2. CIRES, University of Colorado, Boulder, CO 3. CAPS, University of Oklahoma, Norman, OK 4. Physical Sciences Division, ESRL/NOAA, Boulder, CO

2. Introduction • Rapid Refresh (RAP) is an operational hourly updated regional numerical weather prediction system for aviation and severe weather forecasting • GSI-3DVar is used for RAP data assimilation Stephen S. Weygandt: Recent Rapid Refresh Enhancements to Improve Forecast Guidance for Aviation Weather Hazards and Improve Initial Fields for High Resolution Rapid Refresh Forecasts. 9.1 in 16th Conference on Aviation, Range, and Aerospace Meteorology Thursday, 10 January 2013: 8:30 AM. • RAP evolves to a 6-member North American Rapid Refresh Ensemble in the future (2016) • Testing of anhourly updating EnKF-3DVAR hybrid or EnKF capability for the RAP is underway • OU/CAPS, ESRL, and NCEP/EMCcollaboration

3. RAP hybrid/EnKF: Benefits • High-resolution hourly update cycles Situational Awareness NWP = flow dependent • For surface and low level weather system • highly localized system • Vertical flow dependence, much needed for good surface data analysis • For cloud analysis and severe weather • Anisotropic distribution • Build better situation-dependent balance among T, Q and cloud variables in analysis increment

4. RAP hybrid/EnKF: Challenges • High-resolution hourly update cycles • Huge computation cost • Short cut-off time: ensemble forecast needs to be done within a short time • Ensemble convergence fast in hourly analysis • For surface and low-level weather systems • Ensemble spread is usually poor in low levels • For cloud analysis and severe weather • Ensemble requires special physical configuration suitable for cloud and severe weather analysis

5. Experiment system 1: RAP Hybrid System using RAP Ensemble Using RAP configuration to build an hourly cycling 2-way hybrid system for testing the future implement of the Rapid Refresh Ensemble • Same 13km resolution and domain as operation RAP • Hourly updated cycling with GSI Hybrid (2way) and EnKF • Cold starts at 03Z May 30, 2012 and continue cycling 3 days • 40 ensemble members

6. Experiment system 2: RAP GSI hybrid using bkg error cov from GFS Ensemble current real-time RAP configuration Obs Obs Obs GSI 3D-Var GSI 3D-Var GSI 3D-Var HM Obs HM Obs HM Obs Cloud Anx Cloud Anx Cloud Anx 1 hr fcst 1 hr fcst Refl Obs Refl Obs Refl Obs Digital Filter Digital Filter Digital Filter 18 hr fcst 18 hr fcst 18 hr fcst 13 km RAP 14z 13z 15z

7. Experiment system 2: RAP GSI hybrid using bkg error cov from GFS Ensemble 80 member GFS EnKF Ensemble forecast valid at 15Z (9-h fcst from 6Z) Available 4 times a day valid at 03, 09, 15, 21Z Obs Obs Obs GSI Hybrid GSI Hybrid GSI Hybrid HM Obs HM Obs HM Obs Cloud Anx Cloud Anx Cloud Anx 1 hr fcst 1 hr fcst Refl Obs Refl Obs Refl Obs Digital Filter Digital Filter Digital Filter 18 hr fcst 18 hr fcst 18 hr fcst 13 km RAP 14z 13z 15z

8. Single observation test for GSI hybrid using bkg error cov from GFS Ensemble Horizontal cross section of analysis increment from single T obs with 1.0 degree innovation T U V GSI 3D-Var T U V GSI Hybrid (β=0)

9. Real-time Test for RAP hybrid using bkg error cov from GFS Ensemble • Compare RAP development with GSI hybrid to RAP primary cycle with GSI-Var • Real-time test from Nov 22 to Dec 22, 2012 • GSI hybrid with half static BE and half BE from GFS Ensemble forecasts RMS profile for analysis – soundings from 1000-100mb UV T RH RAP hybrid RAP

10. Forecast results: RMS profile RMS profile for 3-h forecast – soundings from 1000-100mb UV RH T RMS profile for 12-h forecast – soundings from 1000-100mb UV RH T RAP hybrid RAP

11. Forecast results: RMS time series RMS time series for 3-h forecast – soundings from 1000-100mb UV T RH RMS time series for 12-h forecast – soundings from 1000-100mb UV RH T RAP hybrid RAP

12. Conclusion • GSI hybrid (using background error covariance from GFS ensemble) is very promising: the statistical results are clearly better than GSI Var • Wind is improved most, next is humidity • Temperature is improved mainly for 3-h but isneutral for 12-h forecast • Middle to upper-air levels show clear improvement but low levels areneutral • Successful ensemble forecasts used by GSI hybrid is key of a successful GSI hybrid analysis • Need to improve RAP hybrid structure

13. Future Work • Tuning parameters • localization, ratio of ensemble BE and static BE, vertical variance of this ratio • GFS ensemble forecast every 1 h rather than 3 h • forecast valid at analysis time • RAP ensemble forecast initialized from GFS EnKF ensemble • Increase spread in low level • Create WRF special physical fields (such as cloud field) • North American Rapid Refresh Ensemble by 2016, co-development between ESRL and NCEP/EMC

14. Initial Test Results from RAP hybrid (EnKF/var) assimilation (1h, 13km) RMS profile for analysis – soundings from 1000-100mb T q UV RMS profile for 3-h forecast – soundings from 1000-100mb T q UV RAP hybrid RAP

15. Diagnosis of RAP hybrid using RAP ensemble Horizontal distribution of Standard Deviation of surface pressure perturbation at 03z, 06z, 09z, 12z, 15z, 18z of May 30, 2012 Time series of prior observation-space ensemble standard deviation 13km Rapid Refresh