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While storm identification and tracking algorithms are used both operationally and in research, there exists no single standard technique to objectively determine performance of such algorithms. Thus, a comparative skill score is developed herein that consists of four parameters, three of which constitute the quantification of storm attributes—size consistency, linearity of tracks, and mean track duration—and the fourth that correlates performance to an optimal postevent reanalysis. The skill score is a cumulative sum of each of the parameters normalized from zero to one among the compared algorithms, such that a maximum skill score of four can be obtained. The skill score is intended to favor algorithms that are efficient at severe storm detection, i.e., high-scoring algorithms should detect storms that have higher current or future severe threat and minimize detection of weaker, short-lived storms with low severe potential. The skill score is shown to be capable of successfully ranking a large number of algorithms, both between varying settings within the same base algorithm and between distinct base algorithms. Through a comparison with manually created user datasets, high-scoring algorithms are verified to match well with hand analyses, demonstrating appropriate calibration of skill score parameters. Significance Statement: With the growing number of options for storm identification and tracking techniques, it is necessary to devise an objective approach to quantify performance of different techniques. This study introduces a comparative skill score that assesses size consistency, linearity of tracks, mean track duration, and correlation to an optimal postevent reanalysis to rank diverse algorithms. This paper will show the capability of the skill score at highlighting algorithms that are efficient at detecting storms with higher severe potential, as well as those that closely resemble human-perceived storms through a comparison with manually created user datasets. The novel methodology will be useful in improving systems that rely on such algorithms, for both operational and research purposes focusing on severe storm detection. [ABSTRACT FROM AUTHOR]

In this paper, a novel technique is proposed to mitigate the so-called blind range on radars that use pulse compression. It is well known that the blind range is caused by the strong leak through into the receiver during the transmission cycle. The proposed technique is called progressive pulse compression (PPC) and is based on partial decoding. PPC uses a portion of the uncontaminated received signal in conjunction with pulse compression to estimate the echoes from the incomplete signal. The technique does not require the use of a fill pulse or any hardware modifications. PPC can be divided into three steps. The first step is to discard all the received signals during the transmit cycle and apply a smooth taper for continuous transition from zero to one. The second step is to perform the pulse compression using matched filter. The combination of these two steps is equivalent to performing pulse compression using a progressively changing template to partially extract the uncontaminated received signal for compression. The third step is to compensate for the progressively changing template so that proper reflectivity values can be recovered. This technique has been tested on the PX-1000 and will be implemented on PX-10k in the near future. These two radars are designed and operated by the Advanced Radar Research Center at the University of Oklahoma and are both X-band software-defined solid-state systems. The results presented in this paper are collected using the PX-1000 radar. [ABSTRACT FROM AUTHOR]