COSMO-DE-EPS

1. COSMO-DE-EPS Susanne Theis, Christoph Gebhardt, Michael Buchhold, Zied Ben Bouallègue, Roland Ohl, Marcus Paulat, Carlos Peralta with support by: Helmut Frank, Thomas Hanisch, Ulrich Schättler, etc

2. NWP Model COSMO-DE • grid size 2.8 km • without parametrization of deep convection (convection-permitting) • lead time 0-21 hours • operational since April 2007 COSMO-DE COSMO-EU GME

3. Plans for a COSMO-DE Ensemble How many ensemble members? • preoperational: 20 members • operational: 40 members When? • preoperational: 2010 • operational: 2012

4. COSMO-DE-EPS production steps • Ensemble products: • - mean • spread • probabilities • - quantiles • ... „variations“ within forecast system ensemble members

5. COSMO-DE-EPS production steps • Ensemble products: • - mean • spread • probabilities • - quantiles • ... „variations“ within forecast system ensemble members + verification + postprocessing

6. COSMO-DE-EPS production steps • Ensemble products: • - mean • spread • probabilities • - quantiles • ... „variations“ within forecast system 1 ensemble members next slides: step 1, generation of members

7. Generation of Ensemble Members Variations in Forecast System for the Representation of Forecast Uncertainty Initial Conditions Boundaries Model Physics

8. Generation of Ensemble Members Variations in Forecast System for the Representation of Forecast Uncertainty Initial Conditions Boundaries Model Physics “multi-model” driven by different global models

9. Generation of Ensemble Members Variations in Forecast System for the Representation of Forecast Uncertainty Initial Conditions Boundaries Model Physics “multi-model” COSMO-DE initial conditions modified by different global models “multi-model” driven by different global models

10. Generation of Ensemble Members Variations in Forecast System for the Representation of Forecast Uncertainty Initial Conditions Boundaries Model Physics “multi-model” COSMO-DE initial conditions modified by different global models “multi-model” driven by different global models “multi-configuration” different configurations of COSMO-DE model

11. Generation of Ensemble Members The Ensemble Chain COSMO-DE 2.8km COSMO 7km COSMO-DE mm/24h global

12. Generation of Ensemble Members • plus the variations of • initial conditions • model physics The Ensemble Chain COSMO-DE 2.8km COSMO 7km COSMO-DE mm/24h global

13. Generation of Ensemble Members • Which computers are used? • at ECMWF: „7 km Ensemble“ • at DWD: COSMO-DE-EPS COSMO-DE 2.8km COSMO 7km global DWD ECMWF

14. Generation of Ensemble Members COSMO 7km • Which computers are used? • at ECMWF: „7 km Ensemble“ • at DWD: COSMO-DE-EPS GME IFS GFS …etc… transfer of data

15. Generation of Ensemble Members COSMO 7km • Which computers are used? • at ECMWF: „7 km Ensemble“ • at DWD: COSMO-DE-EPS GME IFS GFS …etc… • Status: in testing phase (so far: COSMO-SREPS) transfer of data

16. Generation of Ensemble Members • variation of initial conditions

17. Generation of Ensemble Members • variation of initial conditions global forecasts

18. Generation of Ensemble Members • variation of initial conditions ic global forecasts COSMO 7 km COSMO-DE 2.8 km

19. Generation of Ensemble Members COSMO-DE assimilation • variation of initial conditions IC ic global forecasts COSMO 7 km COSMO-DE 2.8 km

20. Generation of Ensemble Members COSMO-DE assimilation • variation of initial conditions modify initial conditions of COSMO-DE by using differences between the COSMO 7km initial conditions IC´ = F (IC, ic – icref) IC ic global forecasts COSMO 7 km COSMO-DE 2.8 km

21. Generation of Ensemble Members • variation of „model physics“

22. 1 2 3 4 5 Generation of Ensemble Members • variation of „model physics“ different configurations of COSMO-DE 2.8 km: entr_sc rlam_heat rlam_heat q_crit tur_len

23. 1 2 3 4 5 Generation of Ensemble Members • variation of „model physics“ selection of configurations: subjective, based on experts and verification selection criteria: 1. large effect on forecasts 2. no „inferior“ configuration different configurations of COSMO-DE 2.8 km: entr_sc rlam_heat rlam_heat q_crit tur_len

24. Generation of Ensemble Members • future changes - extension to 40 members - switch to ICON as driving ensemble (model ICON currently under development) - apply an Ensemble Kalman Filter for initial condition perturbations (EnKF currently under development for data assimilation)

25. COSMO-DE-EPS production steps • Ensemble products: • - mean • spread • probabilities • - quantiles • ... „variations“ within forecast system 2 ensemble members next slides: step 2, generating „products“

26. Generation of „Ensemble Products“ • variables (list will be extended): • 1h-precipitation • wind gusts • 2m-temperature • ensemble „products“: • probabilities • quantiles • ensemble mean • min, max • spread 2 GRIB1 • ensemble products: • mean • spread • probabilities • - quantiles • ... GRIB2

27. Generation of „Ensemble Products“ • further improvement: • adding a spatial neighbourhood • adding simulations started a few hours earlier

28. Generation of „Ensemble Products“ • further improvement: • adding a spatial neighbourhood • adding simulations started a few hours earlier • additional product: probabilities with upscaling event somewhere in 2.8 km Box event somewhere in 28 km Box %

29. COSMO-DE-EPS production steps • Ensemble products: • - mean • spread • probabilities • - quantiles • ... „variations“ within forecast system ensemble members 3 next slides: step 3, visualization in NinJo

30. Visualization in NinJo • new development: „Ensemble Layer“ • for NinJo version 1.3.6 • released in 2010

31. COSMO-DE-EPS production steps • Ensemble products: • - mean • spread • probabilities • - quantiles • ... „variations“ within forecast system ensemble members

32. COSMO-DE-EPS production steps • Ensemble products: • - mean • spread • probabilities • - quantiles • ... „variations“ within forecast system ensemble members + verification + postprocessing

33. Verification Results • very first aim: Does the ensemble meet some basic requirements? • results: • ensemble spread is present • members are of similar quality • ensemble is superior to individual forecasts GEBHARDT, C., S.E. THEIS, M. PAULAT, Z. BEN BOUALLÈGUE, 2010: Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries. Submitted to Atmospheric Research.

34. Postprocessing / Calibration • Ensemble products: • - mean • spread • probabilities • - quantiles • ... „variations“ within forecast system ensemble members

35. Postprocessing / Calibration • Ensemble products: • - mean • spread • probabilities • - quantiles • ... „variations“ within forecast system ensemble members

36. Motivation for Postprocessing / Calibration • Aim: improve the quality • learn from past forecast errors • derive statistical connections • apply them to real-time ensemble forecasts historical data Forecast Obs real-time forecasts

37. Methods for Postprocessing / Calibration statistical postprocessing • First Approach: logistic regression • ensemble „products“: • - mean • spread • probabilities • - quantiles • ...

38. Methods for Postprocessing / Calibration statistical postprocessing • First Approach: logistic regression Plan: preoperational in 2011 for precipitation • ensemble „products“: • - mean • spread • probabilities • - quantiles • ...

39. Research for Postprocessing / Calibration • in addition: Research at Universities, funded by DWD • University of Bonn: Petra Friederichs, Sabrina Bentzien Methods: Quantile Regression, Extreme Value Statistics • University of Heidelberg: Tilmann Gneiting, Michael Scheuerer Methods: Bayesian Model Averaging, Geostatistics

40. COSMO-DE-EPS production steps • Ensemble products: • - mean • spread • probabilities • - quantiles • ... „variations“ within forecast system ensemble members + verification + postprocessing

41. Plans COSMO-DE-EPS • 2010: start of preoperational phase (20 members) • 2010-2012: further extensions • statistical postprocessing • 40 members • 2012: start of operational phase convection-permitting ensemble  operation