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Developing an Ensemble Prediction System based on COSMO-DE

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  1. Project COSMO-DE-EPS Deutscher Wetterdienst Developing an Ensemble Prediction Systembased on COSMO-DE Susanne Theis, Christoph Gebhardt, Michael Buchhold, Marcus Paulat, Roland Ohl, Zied Ben Bouallègue, Carlos Peralta

  2. Deutscher Wetterdienst • Overview • Motivation for Project COSMO-DE-EPS • Tasks within Project • Current Status • where to get information

  3. Deutscher Wetterdienst Motivation for the Project

  4. Deutscher Wetterdienst • Numerical Weather Prediction Model COSMO-DE • grid box size: 2,8 km • without parametrization of deep convection • assimilation of radar data • lead time: 0-21 hours • operational since April 2007 COSMO-DE COSMO-EU GME

  5. Deutscher Wetterdienst Benefits of COSMO-DE COSMO-EU (7 km) COSMO-DE (2,8 km) captures extremes mm/h

  6. Deutscher Wetterdienst Benefits of COSMO-DE COSMO-EU (7 km) COSMO-DE (2,8 km) looks more realistic mm/h

  7. Deutscher Wetterdienst What about Deterministic Predictability? Atmosphere is a chaotic system (Lorenz,1963) lead time

  8. Deutscher Wetterdienst Scale Diagram Predictability Zyklonen 1 Woche typical time scale Cumulo-nimbus 1 Stunde 100 m 10 km 1000 km typical spacial scale lead time

  9. Deutscher Wetterdienst COSMO-DE (2,8 km) mm/h

  10. Deutscher Wetterdienst COSMO-DE (2,8 km) • Need for probabilistic approach •  so that user really gets benefit mm/h  paradigm shift from deterministic to probabilistic prediction

  11. Deutscher Wetterdienst Project COSMO-DE-EPS:Development of a convection-allowing Ensemble Prediction SystemAims and Tasks

  12. Deutscher Wetterdienst Setting up an Ensemble System several predictions = „members“ of ensemble  very large investment of computing power „variations“ in prediction system • ensemble products: • probability density fct • - exceedance probs • quantiles • - „spread“ • mean • ...

  13. Deutscher Wetterdienst • How many ensemble members? • 20 members, pre-operational • 40 members, operational • When? • 2010 pre-operational • 2011 operational

  14. Deutscher Wetterdienst • Tasks within Project • implementation of perturbations • post-processing • verification & diagnostics • visualization • early user feedback

  15. Deutscher Wetterdienst • Tasks within Project • implementation of perturbations • post-processing • verification & diagnostics • visualization • early user feedback

  16. Deutscher Wetterdienst Implementation of Perturbations

  17. Deutscher Wetterdienst Implementation of Perturbations X X initial conditions boundary conditions model physics

  18. Deutscher Wetterdienst Perturbation of the Model

  19. Deutscher Wetterdienst Perturbation of the Model Alter parameters in physics parametrizations entr_sc=0.0003  entrain_sc=0.002 rlam_heat=1.  rlam_heat=10. rlam_heat=1.  rlam_heat=0.1 …q_crit… …tur_len… 1 2 3 4 5 5 4 3 2 1 requires careful tuning  affect forecast, but not long-term quality

  20. Deutscher Wetterdienst Perturbation of Boundary Conditions

  21. Deutscher Wetterdienst Perturbation of Boundary Conditions Reminder: operational chain COSMO-DE COSMO-EU GME

  22. Deutscher Wetterdienst Perturbation of Boundary Conditions Part of AEMet Ensemble (AEMet, Spain) Part of COSMO-SREPS (ARPA-SIMC, Bologna) COSMO-DE-EPS (DWD, Germany) (Garcia-Moya et al., 2007) (Marsigli et al., 2008) (Gebhardt et al. 2009)

  23. Deutscher Wetterdienst Perturbation of Boundary Conditions Part of AEMet Ensemble (AEMet, Spain) Part of COSMO-SREPS (ARPA-SIMC, Bologna) COSMO-DE-EPS (DWD, Germany) GME IFS UM NCEP COSMO-DE (2,8km) COSMO COSMO COSMO COSMO

  24. Deutscher Wetterdienst Perturbation of Boundary Conditions Part of AEMet Ensemble (AEMet, Spain) Part of COSMO-SREPS (ARPA-SIMC, Bologna) COSMO-DE-EPS (DWD, Germany) GME IFS UM NCEP COSMO-DE (2,8km) COSMO COSMO COSMO COSMO long-term Plan: take boundaries from ICON Ensemble

  25. Deutscher Wetterdienst • Perturbation of Initial Conditions • first experiments: perturb “nudging” at forecast start • current work: use differences between control and COSMO-SREPS • plans: Ensemble Transform Kalman Filter (COSMO project KENDA)

  26. Deutscher Wetterdienst Current Status of Perturbation X X initial conditions boundary conditions model physics

  27. Deutscher Wetterdienst Back to the List of Tasks

  28. Deutscher Wetterdienst • Tasks within Project • implementation of perturbations • post-processing • verification & diagnostics • visualization • early user feedback

  29. Deutscher Wetterdienst • More Information • poster at this COSMO General Meeting • past meetings: download presentations • http://www.metoffice.gov.uk/conference/srnwp_workshop/presentations/session_3/18_Theis_EPS09.pdf • http://www.pks.mpg.de/~dswc09/  contribution files • future meetings: ECAM/EMS, SRNWP/EWGLAM, COSMO User Seminar • publications: • Gebhardt et al (2008): Experimental ensemble forecasts of precipitation based on a convection-resolving model. Atmospheric Science Letters 9, 67-72. • Gebhardt et al (2009): Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries. Submitted to Atmospheric Research. • contact: • susanne.theis@dwd.de, christoph.gebhardt@dwd.de

  30. Deutscher Wetterdienst Extra Slides

  31. Deutscher Wetterdienst Current Experiments

  32. Deutscher Wetterdienst Current Status X X initial conditions boundary conditions model physics

  33. Deutscher Wetterdienst 1 2 3 4 5 IFS GME NCEP UM Current Experiments no perturbation of initial conditions

  34. Deutscher Wetterdienst Current Experiments 1 2 3 4 5 IFS GME NCEP UM no perturbation of initial conditions

  35. Deutscher Wetterdienst 2 July 2007, 00 UTC + 24h 24h accumulations of precipitation [mm] mm/24h

  36. Deutscher Wetterdienst Current Experiments 1 2 3 4 5 IFS GME NCEP UM no perturbation of initial conditions

  37. Deutscher Wetterdienst 2 July 2007, 00 UTC + 24h 24h accumulations of precipitation [mm] mm/24h

  38. Deutscher Wetterdienst Ensemble Diagnostics - how do perturbations affect the forecast?

  39. Deutscher Wetterdienst • Effect of perturbations on precipitation • boundaries: • dominate after a few hours • (not necessarily, also case-dependent) • physics: • dominate during first few hours (Gebhardt et al., 2009)

  40. Deutscher Wetterdienst Ensemble Dispersion (precipitation) Normalized Variance Difference = variance (BC only) - variance (PHY only) normalized by sum of variances (Gebhardt et al., 2009)

  41. Deutscher Wetterdienst Ensemble Dispersion (precipitation) Normalized Variance Difference = variance (BC only) - variance (PHY only) normalized by sum of variances for individual days (Gebhardt et al., 2009)

  42. Deutscher Wetterdienst Ensemble Verification - how good is the Ensemble (at this stage)?

  43. Deutscher Wetterdienst Verification Data Sets RADAR

  44. Deutscher Wetterdienst Verification Data Sets (Ensemble Forecasts) 1 2 3 4 5 IFS GME Period: 1 July – 16 Sep 2008 NCEP UM

  45. Deutscher Wetterdienst Verification Data Sets (Ensemble Forecasts) 1 2 3 4 5 IFS GME Period: 1 July – 16 Sep 2008 NCEP UM

  46. Deutscher Wetterdienst Verification Data Sets (Ensemble Forecasts) 1 2 3 4 5 IFS GME Period: 1 July – 16 Sep 2008 when all 15 members available (60% of days) NCEP UM

  47. Deutscher Wetterdienst • Probabilistic Verification Measures • Traditional: • Brier Score, Brier Skill Score • ROC curve • Reliability Diagram • Talagrand Diagram

  48. Deutscher Wetterdienst Talagrand Diagram 24h precipitation 1 3 5 7 9 11 13 15 Rank

  49. Deutscher Wetterdienst Talagrand Diagram 24h precipitation observation 1 3 5 7 9 11 13 15 Rank

  50. Deutscher Wetterdienst • underdispersive ! • add IC perturbations • and statistical PP ! • also look into more • spread measures Talagrand Diagram 24h precipitation 1 3 5 7 9 11 13 15 Rank