An Objective Scoring Method for Evaluating the Comparative Performance of Automated Storm Identification and Tracking Algorithms

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 which 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 which correlates performance to an optimal post-event reanalysis. The skill score is a cumulative sum of each of the parameters normalized from zero to one amongst the compared algorithms, such that a maximum skill score of four can be obtained. The skill score is intended to favor algorithms which 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.