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