1. Short Range Ensemble Prediction System Verification over Greece Petroula Louka, Flora Gofa Hellenic National Meteorological Service

2. COSMO 9th General Meeting HNMS involvement in COSMO-SREPS Task 6 HNMS involvement in COSMO-SREPS Task 6  Verification of LM-COSMO ensemble forecasts for Autumn 2006 21 cases of 72-hour forecast horizon, 16 members Verification domain: Greece Data used: SYNOP data covering Greece Parameters verified: o2m temperature oMean Sea Level Pressure (MSLP) oPrecipitation Statistical analysis of the results Statistical analysis of the results

3. COSMO 9th General Meeting COSMO-SREPS domain COSMO-SREPS domain

4. COSMO 9th General Meeting Greek SYNOP stations 30 stations covering Greece

5. COSMO 9th General Meeting Statistical analysis methods Statistical analysis methods  For continuous parameters such as Temperature and MSLP BiasRMSE  For non-continuous parameters (precipitation) Deterministic approach oMulti-category contingency tables (limits: 0-0.1, 0.1-4.0, 4.0-9.0, >9.0 mm) oPOD, FAR, ETS, etc Probabilistic approach (e.g., ROC diagrams) (average of all 16 members)

6. COSMO 9th General Meeting Point selection Point selection Closest point Interpolated value (1,1)(1,2)(1,3) (2,1)(2,3)(2,2) (3,3)(3,2)(3,1)

7. COSMO 9th General Meeting COSMO-SREPS members COSMO-SREPS members

8. COSMO 9th General Meeting Temperature & MSLP Statistical analysis Temperature & MSLP Statistical analysis  Bias and RMSE averaged over all forecast members and stations

9. COSMO 9th General Meeting 2m Temperature – Bias 2m Temperature – Bias

10. COSMO 9th General Meeting 2m Temperature – RMSE 2m Temperature – RMSE

11. COSMO 9th General Meeting 2m Temperature – Bias 2m Temperature – Bias

12. COSMO 9th General Meeting 2m Temperature – RMSE 2m Temperature – RMSE

13. COSMO 9th General Meeting Mean 2m Temperature SYNOP

14. COSMO 9th General Meeting MSLP – Bias MSLP – Bias

15. COSMO 9th General Meeting MSLP – RMSE MSLP – RMSE

16. COSMO 9th General Meeting MSLP – Bias MSLP – Bias

17. COSMO 9th General Meeting MSLP – RMSE MSLP – RMSE

18. COSMO 9th General Meeting Mean MSLP SYNOP Mean MSLP SYNOP

19. COSMO 9th General Meeting COSMO-SREPS members COSMO-SREPS members

20. COSMO 9th General Meeting Precipitation Statistical analysis Precipitation Statistical analysis  Contingency table  Probability Of Detection (POD) to examine the occurrence of the event  False Alarm Ratio (FAR)  Threat Score (TS) to examine the performance of rare events Limits used: 0-0.1 mm, 0.1-4.0 mm, 4.0-9.0 mm, >9.0 mm

21. COSMO 9th General Meeting Event Forecast Event observed YesNo Total YesHit False alarm Fc Yes NoMiss Correct rejection Fc No Τ otalObs YesObs NoSum total Event Forecast Event observed YesNo Total Yesaba+b Nocdc+d Totala+cb+da+b+c+d=n Precipitation Contingency table Precipitation Contingency table 4x4 table for each 6-hour forecast

22. COSMO 9th General Meeting Precipitation – Example of contingency table Precipitation – Example of contingency table

23. COSMO 9th General Meeting Precipitation – POD 0-0.1 mm Precipitation – POD 0-0.1 mm IFS GME UKMO NCEP

24. COSMO 9th General Meeting Precipitation – POD 0.1-4.0 mm Precipitation – POD 0.1-4.0 mm IFS GME UKMO NCEP

25. COSMO 9th General Meeting Precipitation – FAR 0-0.1mm Precipitation – FAR 0-0.1mm IFS GME UKMO NCEP

26. COSMO 9th General Meeting Precipitation – FAR 0.1-4.0 mm Precipitation – FAR 0.1-4.0 mm IFS GME UKMO NCEP

27. COSMO 9th General Meeting Precipitation – POD Precipitation – POD Tiedtke, pat_len500 Kain-Fritsch, pat_len500 Tiedtke, tur_len1000, pat_len500 Tiedtke, pat_len10000

28. COSMO 9th General Meeting Precipitation – FAR Precipitation – FAR Tiedtke, pat_len500 Kain-Fritsch, pat_len500 Tiedtke, tur_len1000, pat_len500 Tiedtke, pat_len10000

29. COSMO 9th General Meeting Example of Precipitation field 28.0mm 21.0mm 03/11/06LM3 Period 00-06 LM3: IFS, tiedtke (T), tur_len (1000), pat_len (500)

30. COSMO 9th General Meeting Example of Precipitation field 28.0mm 21.0mm 03/11/06LM7 Period 00-06 LM7: GME, tiedtke (T), tur_len (1000), pat_len (500)

31. COSMO 9th General Meeting Example of Precipitation field 21.0mm 28.0mm 03/11/06LM11 Period 00-06 LM11: NCEP, tiedtke (T), tur_len (1000), pat_len (500)

32. COSMO 9th General Meeting Example of Precipitation field 21.0mm 28.0mm 03/11/06LM15 Period 00-06 LM15: UKMO, tiedtke (T), tur_len (1000), pat_len (500)

33. COSMO 9th General Meeting Lightnings observed

34. COSMO 9th General Meeting Satellite image 03/11/2006 Geostationary 00UTC

35. COSMO 9th General Meeting Example of Precipitation field 03/11/06LM3 Period 36-42 LM3: IFS, tiedtke (T), tur_len (1000), pat_len (500)

36. COSMO 9th General Meeting Example of Precipitation field 03/11/06LM7 Period 36-42 LM7: GME, tiedtke (T), tur_len (1000), pat_len (500) 0.0mm >5mm

37. COSMO 9th General Meeting Example of Precipitation field 03/11/06LM11 Period 36-42 LM11: NCEP, tiedtke (T), tur_len (1000), pat_len (500)

38. COSMO 9th General Meeting Example of Precipitation field 03/11/06LM15 Period 36-42 LM15: UKMO, tiedtke (T), tur_len (1000), pat_len (500) 0.5mm >5mm

39. COSMO 9th General Meeting Lightnings observed

40. COSMO 9th General Meeting Satellite image 04/11/2006 Geostationary 12UTC

41. COSMO 9th General Meeting Summary – Suggestions  In general, averaged BIAS and RMSE values showed a small overestimation for MSLP and statistically acceptable values (~ 2C for 2m Temperature and <5 mb for MSLP) apart from a few specific forecasts for which RMSE is large compared to the mean value.

42. COSMO 9th General Meeting Summary – Suggestions  The sample used is statistically small for extracting conclusive information regarding the precipitation forecast  The available results showed that Precipitation amounts are generally overestimated The influence of the different initial conditions on the forecasted precipitation field is evident The influence of convective scheme and turbulent length scale is important mainly on forecasting accurately the presence of a precipitation event (POD value)

43. COSMO 9th General Meeting Future plans  Application of the existed statistical methods to a larger sample  Extend the statistics to include other meteorological parameters  Investigation of precipitation ensemble forecasts using probabilistic approach (ROC diagrams, etc)  Possibility of examining the performance of ensemble forecasting on upper air meteorology