Radar Data Assimilation Using VDRAS and WRF-VAR

1. JuanzhenSun NCAR, Boulder, Colorado Radar Data Assimilation Using VDRAS and WRF-VAR Acknowledgment Hongli Wang Qingnong Xiao Ying Zhang Zhuming Ying Oct 17, 2011

2. Outline • Historical Background • Key findings with VDRAS • Experiences with WRF-VAR - Relative impact of VR and RF • Thoughts on the future Oct 17, 2011

3. Historical Background (VDRAS) • Early works focused on proof of concept - Single Doppler retrieval for the boundary layer (Sun et al 1991, 1994) - Initializing cloud-scale model (Sun and Crook 1997, 1998) • Real-time applications of VDRAS - Real-time nowcasting for NWS, ATEC, DOD,…(Sun & Crook 2001) - Demonstrations in two summer Olympics (Crook and Sun 2004, Sun et al 2010) • VDRAS as a tool for - Understanding convective dynamics and developing conceptual models for nowcasting - Providing predictor fields for Automated nowcasting systems - Initialization of mesoscale models (Liou et al 2011)

4. Historical Background (WRF-VAR) • WRF 3DVAR radar data assimilation - Convective rainfall (Xiao et al 2005, Xiao and Sun 2007) - Tropical cyclone (Xiao et al 2007, Pu et al 2009) - Statistical evaluation over consecutive periods (Xiao et al 2008, Sun et al 2011) - Recent improvement on reflectivity assimilation > Cloud analysis > Assimilate rainwater instead of reflectivity > Use of saturation water vapor as data in the cost function • WRF 4DVAR radar data assimilation - Adjoint of a warm rain microphysics - Control variables of microphysics - Being tested with convective cases

5. VDRAS wind analysis for a study of terrain-induced convection in southern Taiwan 03 UTC - 10 UTC

6. What is the adequate resolution to resolvesome small-scale features? VDRAS continuous analyses of divergence and windFrame interval: 15 min 3KM 1KM

7. VDRAS RTFDDA_d02 Inserting VDRAS analysis into WRF inner domain • Interpolated fields of VDRAS to WRF inner domain • U-wind at the 1st level • Without (left) and with (right) blending of VDRAS & WRF near boundaries 19 UTC 15 June 2002

8. 2-h WRF forecasts valid at 061302 Observation (061302) No VDRAS With VDRAS

9. 5-h WRF forecasts valid at 061302 No VDRAS Observation(061305) With VDRAS

10. VDRAS only OBS VDRAS+WRF WRF only

11. ETS score for accumulated 2-hr rainfall OBS_EC: Using VDRAS alone. WRF : Using WRF alone. OBS_EC+WRF: Combining VDRAS and WRF.

12. Lessons learned by running VDRAS • 10-15 min 4DVAR window seems to be optimal for analyzing • the convective-scale dynamical and thermodynamical • structures • Continuous 4DVAR cycling reveals dynamically consistent • evolution of convective features • The rapid updating and use of derived rainwater avoids • displacement error of storms, a common problem in • microphysical initialization • Radial velocity plays a more important role than reflectivity • VDRAS analysis can be used to initialize mesoscale models • A two-step procedure (non-radar data are used to provide • an improved storm environment before radar DA) enables • a closer fit to radar observations • 1 km resolution resolves much more convective details

13. Study of a supercell storm using a 4DVAR system VDRASSun (2004) Rainwater correlation Radial velocity only Color contour: qr qv Observation w vr and z Observation Forecast vr only Z only Reflectivity only qv w • Without radial velocity, the rain falls • out quickly. • Radial velocity assimilation results • in slantwise updraft and moisture, but • not the reflectivity assimilation

14. Recent WRF 3DVAR Experiments • IHOP June 10 – June 16 one week continuous run • - Active convective period • - 3 hourly update cycle • - 3 km horizontal resolution • - Assimilate 25 radars • - Cloud analysis option • Beijing 4 cases evaluation • - 3 hourly update cycle • - 3 km horizontal resolution • - Assimilate 6 radars • - Assimilate in-cloud saturated water vapor Beijing

15. 6-h Forecasts after four 3DVAR cycles IHOP Results • NORD: Control with no radar DA • RV: Assimilate radial velocity • RF: Assimilate reflectivity • RVRF: Assimilate both One-week FSS skill (5mm) RV RF

16. Beijing Results FSS skill for four 2009 summer cases • NORD: Control with no radar DA • RV: Assimilate radial velocity • RF: Assimilate reflectivity • RVRF: Assimilate both RV RF FSS skill for July 22, 2009 FSS skill for July 23, 2009

17. 2-hour forecasts initialized at 09 UTC on July 22, 2009 CRV CON QPE No Radar OBS CRW RV RF

18. WRF 4DVAR Radar Data Assimilation4-hour forecasts from a case study (13 June 2002) OBS 3DVAR 4D_RV 4D_RF

19. ETS of 0-6 hour forecast 4D_RF 4D_RV 1 mm 3DVAR 5 mm

20. Thoughts on the future • Technical improvement of DA systems • - Observation error statistics – based on information content • - Background error statistics – evolving with system improvement • - Rapid update cycle less than 1 hour for 3DVAR • - Choice of control variables • - Add terrain effect (VDRAS) • Polarimetric radar data assimilation • - Observation operator • - Use estimated microphysics • - Quantify the impact on forecast

21. Thoughts on the future…Continued • Further study of Radar DA impact on convective forecasting • - QPF: dependence on convection type, diurnal cycle, scale, etc… • - Wind, temperature, humidity • Improving accuracy of storm environment • - Operational models that are used as background do not have required • accuracy for convective initiation forecast, especially in the low-level • - Radar clear-air returns do not have adequate coverage • - Make better use of other observations, e.g., surface obs.

22. Diurnal variation of Radar DA impact • Radar DA has longer • positive impact for late • Evening initializations • The positive impact • only lasted 4 hours for • morning initializations • It seems to indicate • that the radar DA works • more effectively for • growing storms than • dissipation storms 00Z 12Z

23. HRRR verification using radiosondes Slides provided by Mei Xu