Storm-Scale Ensemble Forecast Experiment - Fort Worth Tornadic Storm Case

1. Storm-Scale Ensemble Forecast Experiment - Fort Worth Tornadic Storm Case Fanyou Kong1 and Kelvin Droegemeier1,2 1Center for Analysis and Prediction of Storms, 2School of Meteorology, The University of Oklahoma

5. Domain Setting 24km (238x150) 6km (180x180) 3km (180x180)

6. Ensemble perturbation method • Four SLAF (scaled-lagged average forecast) members: s1, s2; s3, s4 perturbations between previous ARPS forecasts (P1,P2) and current analysis are ± to the analysis • One control member (regular ARPS run): cntl

7. 3/28/2000 3/29/2000 00Z 06Z 12Z 18Z 00Z 06Z 24 km ensemble 18-hr cntl 24-hr P1 s1 s2 30-hr P2 s3 s4 6 km ensemble 12-hr rad, sat (P1 – cntl) (P2 – cntl) 3 km ensemble 22Z 8-hr rad, sat

8. 12hr accumulate rainfall ARPS ETA

9. 3-hr rainfall from 24 km ensemble cntl mean spread

10. Domain average spread from 24 km ensemble

11. Domain average spread from 24 km ensemble

12. 3-hr rainfall from 24 km ensemble cntl s1 s2 s4 s3

13. 3-hr rainfall probability from 24 km ensemble ≥ 0.1 in ≥ 0.25 in ≥ 0.50 in

14. 500 hPa Height from 24 km ensemble mean spread

15. Sea Level Pressure from 24 km ensemble mean spread

16. Surface Temperature from 24 km ensemble mean spread

17. 6 km ensemble

18. 3/28/2000 3/29/2000 00Z 06Z 12Z 18Z 00Z 06Z 24 km ensemble 18-hr 24-hr P1 30-hr P2 6 km ensemble 12-hr cntl rad, sat s1 (P1 – cntl) s2 s3 (P2 – cntl) s4

19. 1-hr rainfall from 6 km ensemble cntl mean spread

21. Domain average spread from 6 km ensemble

22. Domain average spread from 6 km ensemble

23. 1-hr rainfall from 6 km ARPS ensemble s1 s2 cntl s3 s4 mean

24. 1-hr rainfall probability from 6 km ensemble ≥ 0.25 in ≥ 0.1 in

25. 1-hr rainfall probability from 6 km ensemble ≥ 1.0 in ≥ 0.5 in

26. Hourly accumulate rainfall (mean vs obs) mean

27. Hourly accumulate rainfall (probability vs obs) Prob ≥ 0.1 in

28. 3 km ensembles • Test different ways to form IC/BC for individual members • Evaluate ensemble analyses and products suitable for storm-scale EF • Assess value of storm-scale EF

29. 3/28/2000 3/29/2000 00Z 06Z 12Z 18Z 00Z 06Z 24 km ensemble 18-hr 24-hr P1 30-hr P2 6 km ensemble 12-hr 3 km ensemble 8-hr cntl (method one & two) rad, sat 22Z s1 (P1 – cntl) s2 s3 (P2 – cntl) s4

30. 3 km Ensembles – Method One • Initiate at 22Z March 28 • Control run from 6km cntl • s1/s2 using perturbation between 24km P1 and control, s3/s4 using perturbation between 24km P2 and control • Run ADAS only once (control run), with NIDS and sat data • Explicit microphysics

31. Composite reflectivity from 3km cntl(initiate at 22Z)

32. radar cntl

33. Method One s1 s3 s2 s4

34. Method One s1 s3 s2 s4

35. 3 km Ensembles – Method Two • Initiate at 22Z March 28 • Control run from 6km cntl • s1/s2 using perturbation between 24km P1 and control, s3/s4 using perturbation between 24km P2 and control • Run ADAS for each member, with NIDS and sat data • Explicit microphysics

36. Method Two s1 s3 s2 s4

37. Method Two s1 s3 s2 s4

38. Surface reflectivity (method two) mean spread

39. Surface reflectivity probability (method two) ≥ 35 dBZ ≥ 45 dBZ

40. Hourly rainfall (method two) s1 s3 obs s2 s4

41. Hourly rainfall probability from 3km ensemble(method two) ≥ 0.1 in ≥ 0.25 in ≥ 0.5 in ≥ 1 in

42. 3 km Ensembles – Method Three • Initiate at 23Z March 28 • Control run from 6km cntl • s1/s2 using perturbation between 24km P1 and control, s3/s4 using perturbation between 24km P2 and control • Run ADAS for each member, with NIDS and sat data • Explicit microphysics

43. Composite reflectivity from 3km cntl(method three)

44. Model reflectivity vs radar cntl radar

45. Model reflectivity vs radar s1 radar

46. Model reflectivity vs radar s2 radar

47. Model reflectivity vs radar s3 radar

48. Model reflectivity vs radar s4 radar

49. Model reflectivity vs radar mean radar

50. Surface reflectivity from 3km ensemble (method three) s1 s2 cntl s3 s4 mean