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COSMO-DE-EPS

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  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, production 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” modification of COSMO-DE initial conditions 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” modification of COSMO-DE initial conditions by different global models “multi-model” driven by different global models “multi-configuration” different configurations of the 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 SREPS data in DWD database 9 8 7 6 5 4 store time minus initial time [hours] transfer of data July / August 2010

  17. Generation of Ensemble Members • variation of initial conditions

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

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

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

  21. 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

  22. Spread vs Lead Time Oct 7 – Nov 24 2009 15 days selected (15 ensemble members) IC pert Measure of ensemble spread: interquartile range of precipitation

  23. Spread vs Lead Time (Case Study) Case Study 18 June 2009 (15 ensemble members) IC pert convective event Measure of ensemble spread: interquartile range of precipitation

  24. 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

  25. 1 2 3 4 5 Generation of Ensemble Members • variation of „model physics“ Selection of Configurations subjective, based on experts, 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

  26. Generation of Ensemble Members • no perturbations of soil moisture (so far) some sensitivity experiments

  27. Soil moisture sensitivity experiments • In IC+PH+BC exp no perturbations at the surface • Use COSMO-EU SO analysis fields: W_SO and/or T_SO • Complete or partially replace SO from COSMO-DE with COSMO-EU fields: WSO_DE = WSO_DE ± C (WSO_EU – WSO_DE), C = 0.5..1 • Start at 06 UTC, 24 hours of forecast • Currently only single runs (no ensemble mode). • Verification for July 2009: Quality of forecast does not decrease

  28. COSMO-DE SO exp 1 cm 6 cm Overall structure similar. Localized differences (rel diffs ~10 %). COSMO-DE does a good job by itself.

  29. COSMO-DE SO Exp Obs. Fcast does not improve (not the point). Want to increase uncertainty range (without being blatantly wrong).

  30. Generation of Ensemble Members Variations within Forecast System: 20 Ensemble Members Model Physics (here still with COSMO-SREPS) (this design will be modified) 1 2 3 4 5 Initial Conditions & Boundaries Lhn_coeff=0.5 IFS GME GFS UM

  31. Case Study: 18 June 2009 Lead times: 0-6 hours 6-12 hours 12-18 hours 18-24 hours mm/6h

  32. Impact of different initial & boundary conditions Derived by global model: IFS (ECMWF) GME (DWD) GFS (NCEP) mm/24h

  33. Impact of different initial & boundary conditions Derived by global model: IFS (ECMWF) GME (DWD) GFS (NCEP) mm/24h

  34. Impact of different model configurations Derived by changing: entr_sc rlam_heat tur_len mm/24h

  35. Impact of different model configurations Derived by changing: entr_sc rlam_heat tur_len mm/24h

  36. 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)

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

  38. 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

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

  40. 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 %

  41. Product Generation: Example Probability of Event „Precipitation > 10 mm/24h“ %

  42. Product Generation: Example Probability of Event „Precipitation > 10 mm/24h“ as usual Derived from: Ensemble Members + Spatial Neighbourhood Defined for: area of 10x10 Grid boxes %

  43. 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

  44. Visualization in NinJo • New Development: „Ensemble Layer“ • for NinJo Version 1.3.6 • released in 2010

  45. Visualization in NinJo NinJo Screenshots

  46. Visualization in NinJo Selection: Ensemble-Layer

  47. Visualization in NinJo Selection: Ensemble Prediction System, Forecast Time, Variable

  48. Visualization in NinJo Selection: Ensemble Prediction System, Forecast Time, Variable

  49. Visualization in NinJo Selection: “Ensemble Product”

  50. Visualization in NinJo Example: Probability of precipitation > 1 mm