SREPS Priority Project: final report

1. SREPS Priority Project:final report C. Marsigli, A. Montani, T. Paccagnella ARPA-SIMC - HydroMeteorological Service of Emilia-Romagna, Bologna, Italy F. Gofa, P. Louka HNMS – Hellenic National Meteorological Service, Athens, Greece

2. Last FTEs of Chiara were used for this TASK

3. Last FTEs of Chiara were used for this TASK ROMEO

4. Outline • COSMO-SREPS methodology • system set-up • analysis of the results on MAP D-PHASE DOP (JJA 2007+SON 2007) • role of different kind of perturbations • error vs spread • boundaries from mm vs different physics • ranking of different driving models and of different physics • conclusions

5. analysis of the results on MAP D-PHASE DOP (JJA 2007+SON 2007) • role of different kind of perturbations • error vs spread • boundaries from mm vs different physics • ranking of different driving models and of different physics • conclusions

6. IFS – ECMWF global COSMO at 25 km on IFS System set-up P1: control (ope) P2: conv. scheme (KF) P3: tur_len=1000 P4: pat_len=10000 GME – DWD global COSMO at 25 km on GME 16 COSMO runs 10 km hor. res. 40 vertical levels by INM Spain UM – UKMO global COSMO at 25 km on UM GFS – NCEP global COSMO at 25 km on GFS 00 UTC JJA=53 SON=54

7. DOP JJA=53 runs SON=54 runs COSMO observations

8. role of different kind of perturbations intra-group distance JJA 2007 - 50 days SON 2007 - 49 days Same model parameters Different driving model Same driving model Different model parameters Z500 COSMO analysis

9. intra-group distanceJJA 2007 -50 days COSMO-SREPS t850 COSMO analysis

10. intra-group distanceSON 2007 -49 days COSMO-SREPS t850 COSMO I7 analysis

11. intra-group distance JJA 2007 - 50 days SON 2007 - 49 days 2mT Northern Italy

13. error vs spread Underdispersive 2m T - relationship between error and spread JJA07 SON07 SYNOP over D-PHASE area - Nearest grid point COSMO I7 analysis

14. Synop stations on the Alpine area 218 stations

15. spread/skill relationship +12h +24h +36h +48h +60h +72h 2mT Alpine area (synop stations)

16. Underdispersive spread/skill relationship +12h +24h +36h +48h +60h +72h 2mT Alpine area (synop stations)

17. t850 relationship between EM error and spread JJA07 COSMO-I7 interpolated on SYNOP stations over the Alpine area

18. t850 relationship between error and spread SON07 COSMO-I7 interpolated on SYNOP stations over the Alpine area

19. spread/skill relationship +12h +24h +36h +48h +60h +72h t850 Alpine area (COSMO analyses)

20. spread/skill relationship +12h +24h +36h +48h +60h +72h t850 Alpine area (COSMO analyses)

21. Z500 relationship between error and spread JJA07 COSMO-I7 interpolated on Northern Italy stations and SYNOP stations over the Alpine area

22. Z500 relationship between error and spread SON07 COSMO-I7 interpolated on SYNOP stations over the Alpine area

23. IT + CH JJA07 noss 1400 700 400 150 50 Same perturbation +30h TP 24h – ave 0.5x0.5 Same driving models +54h Same driving models Same perturbation Big impact of multi model BCs!!!!!!! Looking at the right column it is evident that even with few members the skill does not decrease too much when the driving models are different.

24. IT + CH SON07 noss 800 500 300 100 50 Same perturbation TP 24h – ave 0.5x0.5 Same driving model +30h Same driving model Same perturbation +54h

25. IT IT + CH Same driving model Same perturbation JJA07 +30h

26. JJA07 ecmwf gme ncep ukmo 2m temperature - BIAS p4 Synop observations over Alpine area Nearest grid point – lsm + altitude correction

27. JJA07 ecmwf gme ncep ukmo 2m temperature - MAE p4 Synop observations over Alpine area Nearest grid point – lsm + altitude correction

28. SON07 ecmwf gme ncep ukmo 2m temperature - BIAS p4 Synop observations over Alpine area Nearest grid point – lsm + altitude correction

29. SON07 ecmwf gme ncep ukmo 2m temperature - MAE p4 Synop observations over Alpine area Nearest grid point – lsm + altitude correction

30. Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h 5mm/24h 10mm/24h father

31. Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h 5mm/24h 10mm/24h father

32. Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h 5mm/24h 10mm/24h pert

33. Deterministic scores – ave 0.5 x 0.5 IT 1mm/24h 5mm/24h 10mm/24h pert

34. IFS – ECMWF global Test of more parameter perturbations (same father) P1: control (ope) P2: conv. scheme (KF) P3: parameter 1 P4: parameter 2 P5: … SON 07 16 LM runs at 10 km

35. ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10 T2m deterministic scores – npo IT

36. ctrl KF tur_len=150 tur_len=1000 pat_len=10000 rat_sea=1 rat_sea=60 qc0=0.001 crsmin=50 crsmin=200 c_soil=0 c_soil=2 c_lnd=1 c_lnd=10 rlam_heat=0.1 rlam_heat=10 T2m deterministic scores – npo IT

37. Preliminary Conclusions • Perturbations -Multi Model ICs/BCs & Perturbations on Ph. Params: • the use of different driving models seems to dominate with respect to physics parameter perturbations as regards the contribution to the spread; these contributions are different in the two seasons (2mT) • the selected parameters produce a detectable spread among members with the same father (driving model) • spread-skill relationship: a correlation between error and spread exists, but the system is under-dispersive -> a better representation of model error is needed • the different driving models contribute differently to the ensemble skill, but there is a strong dependence on forecast range, season, verification area • the different perturbations can contribute differently to the ensemble skill as well

38. On-going activities and future plans • continue the analysis over the DOP MAP D-PHASE : • statistical analysis of the system • comparison with the other available mesoscale ensemble systems • verification carried out by HNMS • introduce the new parameter perturbations • analyse the impact of adding soil perturbations

39. COSMO-SREPS methodology driving model perturbations (ics and bcs) - larger scale errors LAM perturbations - smaller scale errors • i.c. and b.c. perturbations -> INM multi-model multi-boundary ensemble (SREPS) • LAM perturbations -> physics parameter perturbations

40. IFS – ECMWF global COSMO at 25 km on IFS System set-up P1: control (ope) P2: conv. scheme (KF) P3: tur_len=1000 P4: pat_len=10000 GME – DWD global COSMO at 25 km on GME by INM Spain 16 COSMO runs 10 km hor. res. 40 vertical levels UM – UKMO global COSMO at 25 km on UM GFS – NCEP global COSMO at 25 km on GFS

41. MAP D-PHASE DOP testing period • COSMO-SREPS was running during the DOP, at 00 UTC • 107 runs out of 183 days, 53 in JJA and 54 in SON • ensemble verification

42. JJA07

43. intra-group distanceJJA 2007 -50 days COSMO-SREPS Same model parameters Different driving model Same driving model Different model parameters 2mT Northern Italy

44. intra-group distanceJJA 2007 –50 days Same model parameters Different driving model Same driving model Different model parameters tp6h Northern Italy

45. Mid Term comments • Mid-upper troposphere: MULTI MODEL IC/BCs give the bigger contribution to the spread • Surface/lower troposphere: model physics perturbations “gain ground”.

46. tp24 JJA07 score evaluation IT • +30h: • ROC • UKMO and GME the best, then ECMWF and NCEP • P2 (KF) the best, then P4, P3, P1 (similar) • BSS • UKMO the best, ECMWF and GME the worst; NCEP improves with threshold • P are similar, P2 (KF) slightly better

47. tp24 JJA07 score evaluation IT • +54 : • ROC • similar for low thresholds, NCEP the best for high thresholds, then ECMWF • P similar, P3 slightly better • BSS • ECMWF the best, GME the worst; NCEP improves with threshold • P2 (KF) the worst, P3 the best but similar to P1 and P4

50. IT JJA07 noss 700 350 200 60 20 pert +30h TP 24h – ave 0.5x0.5 father +54h father pert