Journal Highlights

Assessing sensitivities in algorithmic detection of tropical cyclones in climate data

Editors’ Highlight—

This is a valuable research study that presents a sensitivity analysis for evaluating model input definitions in an automated algorithm for detecting tropical cyclones (TCs) in climate datasets. As emphasized by the authors, multiple tracking techniques are currently used when examining TC changes between the current climate and global warming scenarios. Although broadly similar, slight variations in the TC detection and tracking methods can lead to differences in performance, as well as discrepancies in the magnitude and direction of change in TC statistics, such as number of TCs, number of TC days, and accumulated cyclone energy. Therefore, it is crucial to develop optimized techniques to track TCs that can be more consistently applied across multiple research studies to facilitation model intercomparisons and reduce the artificial noise due imperfect tracking algorithms.  The results of this paper could substantially shorten the time it takes for others to track TCs in climate models. As this is a particularly time/computing intensive endeavor, anything to reduce the load but improve the outcome is worthy of highlighting as a special significance.  


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