Identification of Critical Circulation Patterns in observational and RCM generated Data

1. Identification of Critical Circulation Patterns in observational and RCM generated Data M.Mahboob Alam,András Bardossy DeBilt-15th May 2008 Institute of Hydraulics Univ. Stuttgart,Germany

2. Contents • Objectives • Introduction of the Classification system • Data • Results • Future Plans

3. Objectives • Identification of Critical Circulation patterns associated with extreme events through objective classification of Circulation types (WP5.4 b) • Validation of Extreme Events and assessment of changes of extreme events in RCM generated data (WP5.4 d)

4. Methodology • Objective Classification • Fuzzy rule based classification system (Bárdossy,1995) • Objective Function • Optimal classification • Each class as homogenous as possible • Difference between the classes as big as possible

5. Objective Function • Achieve set of CPs which exaplain variability of Precipitation (ppt.) • Two Objective Functions are used • Dealing with Probability of ppt. • Dealing with the amount of ppt.

6. Objective Function....Contd. • Both the Objective Function are combined by taking a weighted sum

7. Wetness Index WI(-) • Is a measure of Wetness in a given classification • Ratio of % of annual precipitation total and precipitation total for a given CP And Occurance frequency of CP • Higher WI -> wetter CP • contribution of precipitation increase for a given CP and its occurance frequency

8. Data • RT5 generated gridded observational data • RT2B generated RCM data • NCEP data • Time period • Observational data-1950-2000 • RCM data-1958-2000 (ERA40 driven) • RCM data-1958-2050 (ECHAM5 driven) • Data Resolution • 50km,25km,0.5°,0.25°

9. Area of Investigation • German part of Rhein Basin • Tot. Area of basin= 185,000 Km2 • Area of German part=100,000Km2

10. Identified Critical CP‘s • RT5-Gridded observational data set • Time period 1950-2000 • Based on RT5‘s best estimate data on 0.5° resolution for Rhein Basin • Daily mean precipitation data at 83 grid points being considered as station data • 12 CP‘s are classified

11. Wetness indices of some of the stations

13. WI=1.92

14. WI=1.78

15. Driest CP • WI=0.36

16. Precipitation Indices • Pav Mean Precipitation • Pqnn nnth percentile of rainday amounts

17. Precipitation Indices RT5 observational Data • Pn10mm No. Of days >= 10mm • Pxcdd Max. No. Of Consecutive dry days • Pxcwd Max. No. Of Consecutive wet days

18. Precipitation Indices RT5 observational Data • Px3d Greatest 3-day total rainfall • Px5d Greatest 5-day total rainfall

19. RCM Generated Data • KNMI‘s Generated RCM data based on ERA40 Reanalysis is used • Optimized CP definition from Obeservational data is used for classification of CP‘s

20. Wetness Indices

21. Wetness Indices

22. Wet CPs • WI=2.27 • WI=1.86

23. WI=0.30

24. Precipitation Indices RT2B-ERA40 • Pav Mean Precipitation • Pqnn nnth percentile of rainday amounts

25. Precipitation Indices RT2B-ERA40 • Pn10mm No. Of days >= 10mm • Pxcdd Max. No. Of Consecutive dry days • Pxcwd Max. No. Of Consecutive wet days

26. Precipitation Indices RT2B-ERA40 • Px3d Greatest 3-day total rainfall • Px5d Greatest 5-day total rainfall

27. RT5 vs RT2B

28. RT5 vs RT2B

29. RT5 vs RT2B

30. Near Future Plans • CP identification through geopotential heights at different pressure levels • Comparison of observational gridded data with other RCM‘s generated data • Identification of critical circulation patterns related with droughts • Subclassification of identified critical CP‘s