This article introduces a new approach to studying cloud radiative feedback and its impact on tropical cyclone (TC) intensification. The authors propose a linear Variational Encoder-Decoder (VED) model that can learn the hidden relationship between radiation and surface intensification in realistic simulated TCs. By limiting the model inputs, they are able to use its uncertainty to identify periods when radiation plays a more important role in intensification.
The findings from this study suggest that both longwave radiative forcing from inner core deep convection and shallow clouds contribute to TC intensification, with deep convection having the most overall impact. The researchers also highlight the significance of deep convection downwind of shallow clouds in the intensification of specific TCs, such as Haiyan.
This research showcases the potential of machine learning in uncovering thermodynamic-kinematic relationships without relying on axisymmetric or deterministic assumptions. By utilizing the VED model, the authors demonstrate the objective discovery of processes that lead to TC intensification under realistic conditions.
This study provides valuable insights into the complex interactions between radiation and TC intensification, shedding light on the mechanisms that drive these processes. The use of machine learning techniques offers a promising avenue for further exploration and understanding of TC dynamics. Future research could focus on refining and expanding the VED model to analyze real-world TC data and validate the identified thermodynamic-kinematic relationships.