1. Technical Interchange Meeting – ROC / NSSL / NCAR ROC / NSSL / NCAR TIM Boulder CO 11 May 2005 Real-time time-series implementation of the Radar Echo Classifier (REC) for clutter detection in ORDA Mike Dixon NCAR

2. Goal The goal was to develop and test a version of the REC with the following properties: Fast and efficient for real-time operation, suitable for use in the ORDA. Works with time series data, so that the algorithm has access to the spectral domain. Is suitable for detecting clutter and AP.

3. Beam processing sequence Beam 1Beam 2Beam3Beam 4Beam 5 IN Out Beam Queue Compute Moments Compute REC Filter Clutter Filtered Moments out

4. REC for clutter or AP detection Kernel: 5 deg wide, 2 km along the beam. Uses the following fields: –TDBZ - DBZ texture: squared change in dBZ from one gate to the next, in range, averaged over the kernel. –SPIN - DBZ ‘spin’: measure of how frequently the trend in reflectivity along a beam changes with range. Averaged over the kernel. –VEL: velocity at the gate. –SDVE: standard deviation of velocity over the kernel. –WIDTH: spectrum width at the gate. –CLUTPROB: clutter probability, based on ratios of power near 0 m/s to power in rest of spectrum.

5. Membership functions 0 45 1000 1 0 TDBZ SPIN 0 50 100 0 1 0 3.2 1 0 WIDTH 0 0.7 SDVE 0 1 VEL -2.3 0 2.3 0 1 0 3 15 CLUTPROB 0 1

6. Data sets This implementation of the REC was developed to handle time-series data in LIRP format. It was tested on the following data sets: KJIM, stratiform situation, non-phase-coded. SPOL at Boulder, convective situation, phase-coded. SPOL at NAME, Mexico, convective situation, non-phase-coded, alternating-pulse dual-polarization.

7. KJIM Case Non-phase-coded data Stratiform rain to NW Ground clutter

8. KJIM dBZ

9. KJIM Vel

10. KJIM WIDTH

11. KJIM TDBZ

12. KJIM SPIN

13. KJIM SDVE

14. KJIM Clutter Probability

15. KJIM REC

16. KJIM Clutter Flag

17. KJIM dBZ

18. KJIM dBZ filtered

19. KJIM VEL

20. KJIM VEL filtered

21. KJIM WIDTH

22. KJIM WIDTH filtered

23. KJIM filter everywhere

24. KJIM dBZ

25. KJIM dBZ filtered everywhere

26. KJIM vel

27. KJIM vel filtered everywhere

28. SZ Case - SPOL SZ864 decoding Strong mountain ground clutter Convective weather situation

29. SZ dBZ

30. SZ VEL

31. SZ WIDTH

32. SZ Trip flags

33. SZ TDBZ

34. SZ SPIN

35. SZ SDVE

36. SZ REC

37. SZ REC Clutter Flag

38. SZ Clutter found

39. SZ dBZ

40. SZ dBZ filtered

41. SZ VEL

42. SZ VEL filtered

43. SZ WIDTH

44. SZ WIDTH filtered

45. Dual Polarization Case – SPOL at NAME Alternate-pulse dual polarization Strong ground clutter Some sea clutter at times Convective weather situation

46. Additional REC fields for Dual Pol The following fields were added to the REC for the dual polarization case: –RHOHV – value at the gate. –SD-ZDR – standard deviation of ZDR in range, computed for the single beam only, no azimuth averaging. –SD-RHOHV – standard deviation of RHOHV in range, computed for the single beam only, no azimuth averaging.

47. Membership functions – Dual Pol 0 0.8 0.95 1 0 RHOHV 0 2 3 1 0 SD-ZDR 0 0.02 0.03 1 0 SD-RHOHV

48. Dual-pol dBZ

49. Dual-pol VEL

50. Dual-pol WIDTH

51. Dual-pol TDBZ

52. Dual-pol SPIN

53. Dual-pol SDVE

54. Dual-pol ZDR

55. Dual-pol SDZDR

56. Dual-pol RHOHV

57. Dual-pol SD-RHOHV

58. REC – no dual-pol fields

59. REC with dual pol fields

60. REC FLAG – no dual-pol fields

61. REC FLAG with dual pol fields

62. Dual-pol Clutter found

63. Dual-pol dBZ

64. Dual-pol dBZ filtered

65. Dual-pol VEL

66. Dual-pol Vel filtered

67. Dual-pol WIDTH

68. Dual-pol WIDTH filtered

69. Relative performance considerations Some tests were carried out on the computer time taken by the REC and clutter filtering as compared to moments estimation. The following table shows the number of seconds taken to compute moments, REC and filter clutter for a single PPI for each of the cases. The test machine was a 2.8GHz Pentium IV. These numbers are useful to show the relative costs of each operation. KJIM, filter only where REC > thresh KJIM, filter clutter everywhere SZ, filter only where REC > thresh Dual Pol, filter only where REC > thresh Moments estimation 3.18 9.588.23 REC computation 0.53 1.160.54 Clutter filtering 2.379.892.891.35

70. Further work Clutter filtering – handling residual power. SZ clutter filtering. Tuning the REC for dual polarization data. Pattern matching for spectra in range (NESPA, NIMA). Thank you