A Physical Postidentification Approach for Tropical Cyclone Trackers

Abstract Tropical cyclones (TCs) pose a great threat to coastal communities worldwide. TC tracker, which is designed to identify and trace TC-like structures in meteorological data, plays an important role in understanding the variability of TC activity. However, there are still misidentifications when applying these TC tracker algorithms, and this compromises the accuracy of detection results. Here, we present a novel postidentification approach for TC trackers to enhance their accuracy. This approach is developed based on physical connections between TC motions and the ambient environments via environmental steering flow and beta drift. We found that after applying this physical postidentification approach, the false alarm rate (FAR) values decline significantly in two commonly used TC trackers, namely, the CNRM and TRACK algorithms, which were respectively developed to identify cyclonic structures through different environmental indicators. Specifically, the FAR values decreased by 16.3% in the CNRM algorithm and 8.4% in the TRACK algorithm, while high probability of detection (POD) values were maintained in both trackers. Misidentified tracks are primarily characterized by lower intensities and occur at high latitudes. The results indicate that this physical postidentification approach effectively removes misidentified tracks, particularly those corresponding to extratropical cyclones, while simultaneously preserving correctly detected tracks. Our findings demonstrate that integrating environmental steering flow and beta drift into postidentification for TC trackers can improve the detection accuracy. Furthermore, our postidentification approach offers potential for better assessing how climate change affects TC activities when applied to the TC tracks identified by tracker algorithms based on climate models. Significance Statement This purpose of this study is to present a new postidentification approach to improve the accuracy of tracking tropical cyclones. We develop this postidentification approach using environmental steering flow and beta drift. Our results show that this approach can reduce false alarms effectively and maintain a high rate of detecting real tropical cyclones at the same time. The improvement is crucial for disaster mitigation. In the future, we could apply this approach to the identified tracks based on climate models to better understand how climate change might affect tropical cyclone activity.