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

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.

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

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.

Membership functions TDBZ SPIN WIDTH SDVE 0 1 VEL CLUTPROB 0 1

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.

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

KJIM dBZ

KJIM Vel

KJIM WIDTH

KJIM TDBZ

KJIM SPIN

KJIM SDVE

KJIM Clutter Probability

KJIM REC

KJIM Clutter Flag

KJIM dBZ

KJIM dBZ filtered

KJIM VEL

KJIM VEL filtered

KJIM WIDTH

KJIM WIDTH filtered

KJIM filter everywhere

KJIM dBZ

KJIM dBZ filtered everywhere

KJIM vel

KJIM vel filtered everywhere

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

SZ dBZ

SZ VEL

SZ WIDTH

SZ Trip flags

SZ TDBZ

SZ SPIN

SZ SDVE

SZ REC

SZ REC Clutter Flag

SZ Clutter found

SZ dBZ

SZ dBZ filtered

SZ VEL

SZ VEL filtered

SZ WIDTH

SZ WIDTH filtered

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

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.

Membership functions – Dual Pol RHOHV SD-ZDR SD-RHOHV

Dual-pol dBZ

Dual-pol VEL

Dual-pol WIDTH

Dual-pol TDBZ

Dual-pol SPIN

Dual-pol SDVE

Dual-pol ZDR

Dual-pol SDZDR

Dual-pol RHOHV

Dual-pol SD-RHOHV

REC – no dual-pol fields

REC with dual pol fields

REC FLAG – no dual-pol fields

REC FLAG with dual pol fields

Dual-pol Clutter found

Dual-pol dBZ

Dual-pol dBZ filtered

Dual-pol VEL

Dual-pol Vel filtered

Dual-pol WIDTH

Dual-pol WIDTH filtered

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 REC computation Clutter filtering

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