2B.2 1 THE IMPACTS OF MULTI-LAG MOMENT PROCESSOR ON A SOLID-STATE POLARIMETRIC WEATHER RADAR B. L. Cheong 1 ,2 , * , J. M. Kurdzo 1 ,3 , G. Zhang 1 ,3 and R. D. Palmer 1 ,3 1 Advanced Radar Research Center, University of Oklahoma, Norman, OK, U.S.A. 2 School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, U.S.A. 3 School of Meteorology, University of Oklahoma, Norman, OK, U.S.A. Abstract Solid-state weather radars generally require pulse compres- sion and blind range mitigation waveforms in order to gain sufficient sensitivity due to the low peak power of transmit- ters and mitigate the near-range data loss due to the long transmit duty cycle, respectively. At the Advanced Radar Research Center (ARRC) of the University of Oklahoma, we have developed a solid-state polarimetric weather radar, the PX-1000, which uses a long waveform for far range obser- vations and short waveform for blind range filling. It should be emphasized here that we typically use a virtually non- tapered waveform, which fully utilizes the capacity of the solid-state transmitters. One of the consequences of data acquisition using long and short waveforms is the abrupt change of signal-to-noise ratio (SNR) at the transition range from short waveform to long waveform. This effect is mani- fested into a discontinuity of cross-pol correlation coefficients (ρ hv ) in range, which makes subsequent data processing, e.g., data interpretation, automated hydrometeor classifica- tion and data assimilation in numerical weather prediction models, more challenging. The multi-mag moment proces- sor, recently developed in the ARRC, is less sensitive to SNR due to its underlying concept of fitting the auto- and cross- correlation estimates to the Gaussian functions without using the auto-correlation estimates at lag-0. In addition, this algo- rithm does not depend on noise estimation because the use of lag-0 auto-correlation is avoided. In this work, we focus on the ρ hv estimation. We found that the multi-lag moment pro- cessor provides superior results in ρ hv estimates compared to the canonical method, especially when the SNR is mod- erate to low (< 20 dB), which is most typical for low-power solid-state weather radars. Several cases will be presented to illustrate the impacts on ρ hv . 1. BACKGROUND Correlation coefficient ρ hv is a measure of the degrees of similarity between the two polarizations, which is related to size, shape and composition of hydrometeors. It is one of the key components in the hydrometeor classification algorithm (HCA) (Park et al., 2009; Zrni ´ c et al., 2001), which is a fuzzy- * Corresponding author address: Boon Leng Cheong, Uni- versity of Oklahoma, Advanced Radar Research Center, 120 David L. Boren Blvd., Rm 4640, Norman, OK 73072-7307; e-mail: boonleng@ou.edu logic based method that uses 6 radar variables, i.e., (1) re- flectivity from the horizontal channel Z , (2) differential reflec- tivity, (3) cross-correlation coefficient, (4) specific differential phase K DP , (5) a texture SD(Z ) derived from Z , and (6) a texture SD(φ DP ) derived from φ DP , to produce hydrome- teor classes. It has been reported that ρ hv can be used to predict signal quality and gauge hail size (Balakrishnan and Zrni´ c, 1990). Correlation coefficient ρ hv can also be used to discriminate rain and wet snow as signal correlation de- creases when particles are wet or irregular in shape (Straka et al., 2000). That is, one can get an accurate assessment of whether the precipitation contains wet snow by inspecting the ρ hv values as wet snow typically has ρ hv that are lower than that of rain. The ρ hv estimate also plays an important role in methods to distinguish meteorological against non- meteorological echoes, e.g., birds, insects and smoke (Tang et al., 2013). At X-band, the output power produced by solid-state ampli- fiers is still relatively low, on the orders of 100’s of watts, and, thus, the returned signals often have moderate to low SNR. As ρ hv degrades with decreasing SNR (Bringi and Chandrasekar, 2001), one can expect that the quality of ρ hv is widely degraded on an X-band solid-state weather radar. The primary motivation of this work is to explore the impacts of the multilag moment processor, which promises improved performance at low SNR regimes that are typically found with solid-state weather radars. 1.1. Signal Processing Method Using the PX-1000, an ARRC in-house-developed 100-W solid-state X-band polarimetric weather radar, raw time se- ries data consisting of digitized in-phase and quadrature (IQ) components are used to derive radar moments and polari- metric variables. The radar uses pulse compression with a time-frequency multiplexed (TFM) waveform to eliminate the blind range due to the use of long transmit cycle (Cheong et al., 2013). The transmit cycle is approximately 10 km in the results that will be presented in this article. Briefly, the transmit waveform is a time multiplex of a long waveform and a short waveform, i.e., a temporal concatenation. The short waveform is appended to the end of the long waveform in this order in order for the technique to be viable as the total blind range is the sum of the two pulse lengths. The two wave- forms use two distinct bands that do not overlap each other.