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An Objective Scoring Method for Evaluating the Comparative Performance of Automated Storm Identification and Tracking Algorithms
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2022
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Source: Weather and Forecasting (published online ahead of print 2022)
[PDF-6.92 MB]
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Journal Title:Weather and Forecasting
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Description: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.
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Source:Weather and Forecasting (published online ahead of print 2022)
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Rights Information:Other
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Compliance:Submitted
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