1. Convection-permitting forecasts initialized with continuously-cycling limited-area 3DVAR, EnKF and “hybrid” data assimilation systems Craig Schwartz and Zhiquan Liu NCAR/NESL/MMM schwartz@ucar.edu NCAR is sponsored by the National Science Foundation

2. Introduction Convection-permitting forecasts have commonly been initialized from operational analyses (e.g., GFS, NAM) – Example: Interpolate GFS analysis onto WRF domain Continuously cycling mesoscale data assimilation systems can produce initial conditions for convection- permitting forecasts – Dynamically consistent analysis/forecast system

3. A few data assimilation approaches Three-dimensional variational (3DVAR) – Background error covariances (BECs) typically fixed/time-invariant – May yield poor results when actual flow differs from that encapsulated within the fixed “climatology” Ensemble Kalman filter (EnKF) – Time-evolving, “flow-dependent” BECs estimated from a background ensemble

4. “Hybrid” variational/ensemble – Incorporates ensemble background errors within a variational framework – Combination of fixed and time-evolving background errors A few data assimilation approaches 75% squirrel 25% cat

5. Experimental design Full-cycling (6-hr period) between May 6 – June 21, 2011 Data assimilation/cycling on a 20-km domain Three experiments assimilating identical observations: Pure 3DVAR Pure EnKF Hybrid 0000 UTC analyses initialized 36-hr 4-km forecasts EnKF: 4-km forecasts initialized from mean analyses Control: Interpolate 0000 UTC GFS analyses directly onto the domain and run forecasts GFS initialized from 3DVAR analyses in 2011

6. Cycling data assimilation: Hybrid/EnKF flowchart

7. Computational domain

8. WRF settings and physics Forecast model: WRF-ARW (version 3.3.1) 57 vertical levels, 10 hPa top Physics: Morrison double-moment microphysics RRTMG longwave and shortwave radiation MYJ PBL Tiedtke cumulus parameterization (20-km domain only) NOAH land surface model Aerosol, ozone climatologies for RRTMG

9. Selected data assimilation settings NCEP’s Gridpoint Statistical Interpolation (GSI) data assimilation system: -GSI-3DVAR -GSI-hybrid -Ensemble square root Kalman filter (EnSRF) 50 ensemble members Hybrid: 75% of the background errors from the ensemble, 25% from the static contribution Used posterior inflation for EnSRF and localization in both EnSRF and hybrid

10. Observation snapshot (0000 UTC 25 May)

11. Precipitation verification Focus on 4-km precipitation forecasts NCEP Stage IV observations as “truth” Verified hourly precipitation forecasts All precipitation statistics shown are aggregated over 44 4-km forecasts Fractions skill score (FSS) quantifies displacement errors

12. Precipitation Bias Aggregated hourly over the first 12 forecast hrs Aggregated hourly over 18-36-hr forecasts

13. FSS: The first 12-hrs 0.25 mm/hr 1.0 mm/hr 5.0 mm/hr10.0 mm/hr

14. FSS: Forecast hours 18-36 0.25 mm/hr 1.0 mm/hr 5.0 mm/hr 10.0 mm/hr

15. For more information… All of the previous material was summarized in this publication: Schwartz, C. S., and Z. Liu, 2014: Convection-permitting forecasts initialized with continuously-cycling limited-area 3DVAR, ensemble Kalman filter, and “hybrid” variational-ensemble data assimilation systems. Mon. Wea. Rev., 142, 716–738, doi: 10.1175/MWR-D-13-00100.1.

16. Preview of new work Recently, the exact same experiments were performed but over a new period: – May 4 – June 30, 2013 – 55 4-km forecasts Near identical configuration as before, except used Thompson microphysics Also performed dual-resolution hybrid analyses with a 4-km deterministic background and 20-km ensemble

17. Cycling data assimilation: Hybrid/EnKF flowchart 4-km 20-km

18. FSS: The first 12-hrs 2013 experiments: FSS aggregated over 55 forecasts 0.25 mm/hr 1.0 mm/hr 5.0 mm/hr 10.0 mm/hr

19. FSS: The first 12-hrs 2013 experiments: FSS aggregated over 55 forecasts 0.25 mm/hr 1.0 mm/hr 5.0 mm/hr 10.0 mm/hr Dual-resolution hybrid: 4-km analyses and subsequent forecasts

20. FSS: Forecast hours 18-36 2013 experiments: FSS aggregated over 55 forecasts 0.25 mm/hr 1.0 mm/hr 5.0 mm/hr10.0 mm/hr

21. Summary Precipitation bias characteristics similar in the cycling experiments Differences in precipitation placement evident – Hybrid and EnSRF performed best – Shows the benefit of flow-dependent background errors Further improvement possible with high- resolution analyses

25. Example forecast 6-hr forecast initialized 0000 UTC 24 May 2011

26. Example forecast 30-hr forecast initialized 0000 UTC 24 May 2011