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Multi-PRI and SMPRF Software Update for Weather Radars

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Multi-PRI and SMPRF Software Update for Weather Radars ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY Multi-PRI and SMPRF software update for weather radars Jörn Sierwald Eigenor Corporation, Lompolontie 1, 99600 Sodankylä, Finland, [email protected] (Dated: 28 May 2012) Jörn Sierwald 1 Introduction The Eigenor WnD software is designed for Multi-PRI1. We have modified the C-band radar at the University of Helsinki, Finland to transmit a pattern of three pulse intervals by programming the Sigmet RVP900™ processor. The existing processor retains all its standard capabilities and the Multi-PRI measurements use the IRIS task scheduling, antenna control and calibration so that WnD measurements can be scheduled alongside the normal tasks of the radar. The WnD software offers the standard products of normal dual-polarization radar but with the benefit of the extended velocity range of Multi-PRI. WnD estimates the autocorrelation function for each volume and calculate velocity distributions to separate precipitation from other objects such as birds or sea clutter. 2 Radar setup 2.1 Kumpula radar We have used the dual-pol C-band Radar built by Vaisala which is located on the campus of the University of Helsinki in Kumpula. It is jointly operated by the University of Helsinki and the Finnish Meteorological Institute. The first Multi-PRI measurement (see chapter 3.5) has been done in May 2011 using a Sigmet RVP8 processor. The system has then been upgraded to a RVP900 processor. In the end of March 2012 the Eigenor WnD measurements were integrated into the IRIS scheduling system. In order to send Multi-PRI patterns, we have modified the software of the processor. The RVP900 is able to generate I/Q time series data which is then sent to another computer for processing. The data processing capabilities of the RVP900 after I/Q generation are not used while running in WnD mode. Apart from the modified sending and the transfer of time series data the system is not modified. Antenna control, calibration and scheduling work as usual. The radar retains all its normal capabilities after installing the Eigenor WnD software. WnD measurements and the standard tasks supplied by Vaisala can be mixed in the scheduler. 2.2 Multi-PRI timing Multi-PRI transmissions allow a radar to measure higher unambiguous velocitiesa. The timing for the transmissions must be precise, as WnD requires that the intervals between pulses have a large common divisor which itself must be a multiple of the bin length. This requirement is caused by the SMPRF system which resolves range ambiguities. For example, the Kumpula radar was supposed to use a range resolution of one microsecond (just under 150 meters), a factor of 250 and an interval ratio of 7:8:10. The intervals thus are 1×250×7, 1×250×8 and 1×250×10 microseconds, in other words 1750 µs, 2000 µs and 2500 µs. As the RVP900 did not allow a configuration in terms of microseconds, a resolution of 150 m is used instead. Because 150 m are slightly longer than one µs the actual timings used are 1751.2 µs, 2001.38 µs and 2501.73 µs. This document uses the rounded values for better readability. The resulting unambiguous velocity is the velocity of an ordinary Doppler radar using a PRI of 250 µs. For the Kumpula installation with a frequency of 5610 MHz the unambiguous velocity range is from -53.5 m/s to +53.5 m/s. Using the ordinary PRF of 580 Hz the radar has an unambiguous velocity range from -7.7 m/s to +7.7 m/s. 3 Software implementation details 3.1 Data input and output The angular resolution of the radar is usually the same as the width of the main lobe of the antenna, in this case 1 degree. The software handles eight “beams” (one beam being the data received during one degree of a revolution of the antenna) in parallel using vectorization. For dual polarization radars it handles four beams in parallel. In a real-time setup the software will wait for enough data to start processing. When real-time processing is not a requirement, WnD reads data for entire revolutions of the antenna at once. Data is to be stored as HDF5, pending a specification for our non-standard products. 1 Multi-PRI – Sending pulses using more than one fixed interval between pulses. For instance intervals of 1000 µs, 1200 µs and 1400 µs and then starting over ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY 3.2 Dual polarization WnD support single and dual polarization radars. For the pulses, WnD require a short repeating pattern. A pattern of n times a short PRI followed by m times a long PRI is not supported. The trivial case of only one PRI is supported. For dual polarization, the radar is expected to send the same output signal over both feeds (single transmitter). 3.3 Calibration Calibration is handled by the processor software. Calibration data is saved with the time series data. 3.4 Ground clutter removal As the first step of processing WnD perform a polynomial fit. The data for one degree of rotation at each range is fitted with a polynomial of a low degree. This is then subtracted from the original signal. The result is a high-pass filter, filtering out ground clutter which changes slowly as the radar turns. As the filter cannot distinguish between ground clutter and useful signal such as rain at speed zero, both the filtered signal and the original are passed on to further processing. Later stages then choose which signal is used. 3.5 SMPRF and data selection The published SMPRF methodb,c,d resolves range ambiguities by solving a set of linear equations. Eigenor has tested this method and has implemented an alternative which selectively discards data which is likely to contain ambiguities. Ranges for 250 µs×[7;8;10] Pulse 0 262,5 112,5 Pulse 1 112,5 187,5 75 Pulse 2 75 300 0 50 100 150 200 250 300 350 400 Range in km Mixed with previous echo Unambiguous Mixed with next echo Figure 1. Usable range for Eigenor WnD standard timing. Figure 1 shows the overlapping echoes of the standard timing. WnD assumes that there are no echoes from beyond 375 km. The red and blue areas are derived from the same I/Q samples, but are different after the random phase processing (WnD sends pulses with random phases). The data selection method works as follows: The green “Unambiguous” range marks data that is free of ambiguities. The blue “Mixed with previous echo” range marks data that has echoes from short ranges mixed with echoes from high ranges. As the echo from beyond 260 km is very likely to be small compared to the low range part WnD use the data from the blue areas in calculations. The red “Mixed with next echo” areas however are discarded. 3.6 ACF estimation WnD calculates an estimate of the autocorrelation function for each volume. The data points of the ACF are not equally spaced. For our standard timing the data points are at 0, 1750, 2000, 2500, 3750, 4250, 4500, 6250, 8000, 8250, 8750, 10000, 10500, 10750, 12500, 14250, 14500, 15000, 16250, 16750, 17000 and 18750 microseconds. ACFs are calculated for both polarizations and across polarizations, with and without ground clutter. 3.7 ACF inversion Calculating a velocity distribution from the ACFs is not straightforward because the data points are not equally spaced. Furthermore, calculating a high-resolution velocity distribution is counterproductive. When calculating a distribution with a range of ±50 m/s and a resolution of 0.1 m/s the vast majority of the distribution will be noise or nothing at all. Precipitation will be spread out over many bins of the histogram. This is pleasant looking to the human eye but classification algorithms will have to combine the data again to have just one object of precipitation. The algorithm chooses a number of model functions and tries to find a linear combination that match the estimated ACF. An example: Choose a uniform grid of 1000 speeds from -50 m/s to +50 m/s. Each speed has a corresponding ACF, which is of the form . Using the method of simulated annealing, calculate a likely linear combination of these model functions which matches the ACF we have measured. The result is a histogram of speeds that can be displayed easily. Rather than trying to work with the histogram of 1000 data points Eigenor has built the data reduction into the simulated annealing. In addition to a fine grid of speeds like the one explained above WnD introduces a range of model functions that ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY have a “width” in the velocity domain. The algorithm will favor model functions with a large width over combinations of functions with a small width. The algorithm will also favor solutions that have a low number of elements. 3.8 ACF inversion using entire beams For performance reasons the Eigenor WnD software processes packages of four beams, each having the width of the main lobe of the antenna. The objects found during ACF inversion can extend over many volumes. As a rule of thumb, objects with a large width in the velocity domain such as rain have a high chance to be present in an adjacent volume. Birds, on the other hand, are unlikely to extend over several volumes. For this reason the ACF inversion of the volumes are not done one-at-a-time but in a pattern that allows detection of objects than span several volumes.
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