Investigating differences between Tropical Cyclone detection systems

Tropical cyclones (TCs) are important phenomena; understanding their behaviour 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 TCDetect has good recall, particularly for hurricane-strength events. The primary question addressed here is how much the structure of the systems plays a part in detection. To compare with observations of TCs, it is necessary to apply detection techniques to re-analysis. 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 with 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 (that is, events detected as having hurricane strength, but being weaker in reality) and extra-tropical storms. As TCDetect was not trained to locate TCs, a post-hoc method to perform comparisons was used. While this method was not always successful, some success in matching tracks and events in physical space was also achieved. The analysis of matches suggested 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.