Characterizing Asymmetric Convection In Rapidly Vs. Non-Rapidly Intensifying Tropical Cyclones Using IR Imagery: An Emphasis On Predictability.

Rapid intensification (RI) of tropical cyclones (TCs) remains a challenge in forecasting. Although the most intense and destructive TCs often undergo RI, one of the primary issues is that, even with the presence of favorable environmental conditions, RI does not always occur, leading to a high number of false alarms. Our study focuses on the characteristics of the inner core convection of TCs as an influence and potential contributor to improve the predictability of rapid intensification (RI). Using infrared high-resolution satellite imagery from GOES-16/17 and Himawari-8, we examine the differences in convection among successful RI forecasts (true positives), false positives, and false negatives selected from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) database. The selection also considered favorable environmental conditions during the lifetime of the storms and their proximity to land, resulting in 4 cases of each of the three types of TCs (true positives, false positives, and false negatives) for the period 2015-2022. We analyze the spatial characteristics and temporal evolution of the identified convective structures within the TCs (including hot towers and rainbands), such as their size, number, duration, and distance from the center, while focusing on the influence of asymmetric convection on the TC intensity. The results reveal differences in the hot towers’ behavior comparing RI and non-RI cases. Rapidly intensifying TCs exhibit a higher number of hot towers that are also larger, last longer, and tend to cluster closer to the storm center. For successfully predicted RI cases (true positives), we observe that the convective towers are often arranged following a spiral pattern around the developing eye, while in false positive cases, although there is a similar initial burst of asymmetric convection around the TC, the hot towers dissipate more quickly and fail to maintain a coherent structure or grow with time. The ability of the towers to merge, resulting from their positioning, persistence, and strength, appears to be a key indicator for distinguishing between actual RI events and false alarms. These findings suggest that quantitative analysis of the characteristics of asymmetric convection in TCs could enhance RI prediction models, potentially reducing false positives and false negatives. This approach could lead to more accurate forecasts and more effective risk mitigation strategies for coastal communities in the context of climate change.