Abstract/Summary

Tropical cyclones (TCs) are important phenomena, and understanding their behavior requires being able to detect their presence in simulations. Detection algorithms vary; here we compare a novel deep learning–based detection algorithm (TCDetect) with a state-of-the-art tracking system (TRACK) and an observational dataset (IBTrACS) to provide context for potential use in climate simulations. Previous work has shown that TCDetect has good recall, particularly for hurricane-strength events. The primary question addressed here is to what extent the structure of the systems plays a part in detection. To compare with observations of TCs, it is necessary to apply detection techniques to reanalysis. For this purpose, we use ERA-Interim, and a key part of the comparison is the recognition that ERA-Interim itself does not fully reflect the observations. Despite that limitation, both TCDetect and TRACK applied to ERA-Interim mostly agree with each other. Also, when considering only hurricane-strength TCs, TCDetect and TRACK correspond well to the TC observations from IBTrACS. Like TRACK, TCDetect has good recall for strong systems; however, it finds a significant number of false positives associated with weaker TCs (i.e., events detected as having hurricane strength but are weaker in reality) and extratropical storms. Because TCDetect was not trained to locate TCs, a post hoc method to perform comparisons was used. Although this method was not always successful, some success in matching tracks and events in physical space was also achieved. The analysis of matches suggested that the best results were found in the Northern Hemisphere and that in most regions the detections followed the same patterns in time no matter which detection method was used.