A new signal processing technology to eliminate range sidelobes in meteorological radar data

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A conventional meteorological radar works by transmitting a pulse of microwaves into the atmosphere, and measuring the echoes from cloud and precipitation particles. The sum of these echoes over many pulses (the "radar reflectivity") can then be used to infer properties of those particles, such as rainfall rate. Radar reflectivity often varies substantially over small distances - for example stratocumulus clouds may only be 200m thick, while the heavy rain from a thunderstorm cell may be only a few kilometres wide. Because of this, high resolution measurements are required. This depends on two things. First the pencil beam from a radar can be made very narrow by using a large antenna, and this is straightforward for most cloud and precipitation radars. Second, we need high resolution along the length of that pencil beam. This "range resolution" depends on the duration of the pulse which the radar transmits. A short pulse leads to high range resolution, which is what we want. A long pulse leads to coarse range resolution which will not resolve the clouds and rain cells we wish to probe. Unfortunately there is a trade-off: long pulses give greater sensitivity to weak echoes, while short pulses give poorer sensitivity. Pulse compression aims to bypass this trade-off. Long pulses are transmitted for high sensitivity, but extra information is encoded into these long pulses on short time scales. The echoes reflected back to the antenna are then "decoded" into the desired high range resolution. This ability to have both high sensitivity and high resolution makes pulse compression an extremely attractive technology for meteorological radars. However it has a major practical drawback, which is the formation of range-sidelobes. Because the decoding process never works perfectly, some information is spread out in range. This leads to corruption of the radar data and erroneous measurements of cloud properties and rainrates. This problem must be solved if pulse compression is to be fully exploited.We have recently developed a simple new technique which may solve this problem. This method, which has a rigorous grounding in statistics and signal processing, allows us to identify where range sidelobes are occurring, and even correct the corrupted data. It transpires that nature provides the solution to the problem, and it does so by causing the particles in clouds and precipitation to "reshuffle" relative to one another every few milliseconds, as a result of turbulence, wind shear, and variations in fall speed. This causes the echo measured by the radar to fluctuate - and every fluctuation is unique, like a fingerprint. To determine where the echo from one range has leaked into data at another range, we look for traces of that fingerprint where it shouldn't be (we correlate two sets of fluctuations). If there is a significant correlation we can identify that corruption of the data has occured. Furthermore, the degree of correlation between the two sets of fluctuations tells you how much power has leaked from one to the other, and this knowledge allows us to correct for that leakage. So far our work has been a theoretical analysis, and its application to two short samples of data from a cloud radar. The proposed project would develop the idea to the point where we can demonstrate that this is likely to be a practical technology of use for both research and national weather service radars. We will underpin our theory with calculations of the errors in our correlation measurements - this determines how accurately we can identify and correct our data. Second, many radars operate 24/7 so we will demonstrate that the technique is computationally fast enough to operate in real-time. Third, weather services use scanning radars which transmit "dual-polarised" pulses and this creates some challenges for the technique: we will demonstrate how it can be applied such systems.