1. WWOSC 2014 Assimilation of 3D radar reflectivity with an Ensemble Kalman Filter on a convection-permitting scale WWOSC 2014 Theresa Bick 1,2,* Silke Trömel 1,2, Kathrin Wapler 1,3, Clemens Simmer 2 1 Hans-Ertel-Centre for Weather Research, Atmospheric Dynamics and Predictability Branch 2 Meteorological Institute, University of Bonn, Germany 3 German Meteorological Service, Offenbach, Germany * thbick@uni-bonn.de Special thanks to K. Stephan, Y. Zeng, R. Potthast, H. Reich, H. Lange August 18, 2014 1

2. WWOSC 2014 Motivation Why radar data assimilation?  Highly resolved in space and time, dense coverage  3D information of convective systems  Improve short-term model forecasts of high impact weather events Why ensembles?  No Ad/TL model or linearization necessary  Flow-dependent covariances 2

3. WWOSC 2014 COSMO-DE  Convection-permitting numerical weather prediction model  Δx = 2.8km, 50 vertical layers  Domain size: ~ 1200 km x 1300 km KENDA (Km-scale ensemble data assimilation):  Local Ensemble Transform Kalman Filter (LETKF) for COSMO-DE (Reich et al, 2011, following Hunt et al, 2007) 3

4. Radar forward operator Observation  Radial velocity v r  Reflectivity Z e  Radar grid: range, azimuth + elevation WWOSC 20144 COSMO-DE  Temperature T  Wind components U, V, W  Mixing Ratios QR, QS, QG,..  COSMO-DE model grid Radar forward operator derives pseudo radar volume scan from COSMO-DE model output (Zeng 2013)

5. No-reflectivity  Assimilate clear air information  Constrain all values to 5dBZ  Huge amount of data:  Superobbing: reduce observation density (cf. talk Y. Zeng)  Relaxation to prior spread: maintain ensemble spread after analysis (Harnisch and Keil 2014, submitted to MWR following Whitaker and Hamill, 2012) WWOSC 20145

6. Cycling Experiment Experiment RUC:  Assimilated observations: Reflectivity, no-reflectivity  Update every 15 minutes  Superobbing: Δx = 10km WWOSC 20146 Experiment CNTRL:  Assimilated observations: synop, temp, airep  Hourly update Case study: June 6th 2011  3h cycling (12-15UTC) followed by 6h free forecast (15-21UTC)  40 ensemble members

7. Analysis mean vs observation WWOSC 20147 ObservationRUCCNTRL

8. 3h forecast mean (ini 15 UTC) vs observation WWOSC 20148 ObservationRUCCNTRL

9. 3h forecast: “good” members vs. observation WWOSC 20149 ObservationRUCCNTRL

10. 3h forecast: “bad” members vs. observation WWOSC 201410 ObservationRUCCNTRL

11. Fraction skill score WWOSC 201411  Use FSS (Roberts and Lean, 2007) to verify forecast against radar measurement  Convert model and observation into binary fields (exceedance of dBZ-threshold)  Generate fractions of nearest neighbors for every grid point  Range: 0 to 1, perfect: 1 Radar 1, 8/21 Model 0, 6/21

12. FSS for different scales WWOSC 201412 16UTC (1h fc) 21UTC (6h fc) RUC CNTRL

13. FSS: forecast window WWOSC 201413 10km radius 20km radius 200km radius RUC CNTRL

14. RMSE: Unobserved variables WWOSC 201414 RUC CNTRL T 2m U 10mV 10m Rel.hum 2m

15. Summary  Radar reflectivity assimilation in KENDA (LETKF for COSMO-DE) has a positive impact on the analysis:  Precipitation patterns occur with smaller displacement  Assimilating „no-reflectivity“ suppresses spurious convection  Analysis does not deteriorate unobserved variables  During forecast….  Cells produced by analysis survive for several hours  FSS indicates clear benefit of radar reflectivity assimilation on small scales, slight disadvantage on larger scales  RMSE of unobserved variables evolves similarly to control run WWOSC 201415

16. Outlook  Verification of more case studies  Analysis only yields improvement when ensemble spread is large  Additive noise to allow for non-linear development  Combination of radar reflectivity with other observation types:  Radial velocity  Polarimetric moments, inference on mixing ratios (need for 2 moment schemes?)  Cloud information to predict convective initiation WWOSC 201416

17. WWOSC 2014 Assimilation of 3D radar reflectivity with an Ensemble Kalman Filter on a convection-permitting scale WWOSC 2014 Theresa Bick 1,2,* Silke Trömel 1,2, Kathrin Wapler 1,3, Clemens Simmer 2 1 Hans-Ertel-Centre for Weather Research, Atmospheric Dynamics and Predictability Branch 2 Meteorological Institute, University of Bonn, Germany 3 German Meteorological Service, Offenbach, Germany * thbick@uni-bonn.de Special thanks to K. Stephan, Y. Zeng, R. Potthast, H. Reich, H. Lange August 18, 2014 17

18. FSS for different scales. Threshold 10dBZ WWOSC 201418 16UTC (1h fc) 21UTC (6h fc) RUC CNTRL

19. FSS: forecast window. Threshold 10dBZ WWOSC 201419 10km radius 20km radius 200km radius RUC CNTRL

20. FSS for single grid points: 16UTC, scale = 200km WWOSC 201420

21. FSS for single grid points: 21UTC, scale = 200km WWOSC 201421

22. Ensemble spread after analysis WWOSC 201422

23. How to treat errors in inital conditions? First trial. Experiment RUC:  Reflectivity, no- reflectivity  Update every 15 minutes  Δx = 2.8km WWOSC 201423 Experiment CNTRL:  Synop, temp, airep  Hourly update Case study: June 6th 2011, radar station Essen  2h cycling of a single cell over North-Rhine Westphalia  40 ensemble members Exp. Add. noise:  Reflectivity, no- reflectivity  Update every 15 minutes  Δx = 2.8km  Random noise in locations where Z obs > Z thresh

24. Analysis vs. observation WWOSC 201424 ObservationRUCCNTRL Add. noise

25. Experiment RUC – 1h earlier WWOSC 201425 ObservationEns. spreadAnalysis incr.

26. Experiment Add. noise – 1h earlier WWOSC 201426 ObservationEns. spreadAnalysis incr.