Identifying Three-Dimensional Radiative Patterns Associated with Early Tropical Cyclone Intensification
This work addresses the challenge of understanding asymmetric radiative feedbacks in tropical cyclone intensification for meteorologists, representing an incremental advance by applying machine learning to improve diagnostic frameworks.
The researchers tackled the problem of identifying 3D radiative patterns linked to early tropical cyclone intensification, using a linear Variational Encoder-Decoder to analyze simulated data and finding that longwave radiative forcing from deep convection and shallow clouds contributes to intensification, with deep convection having the most impact overall.
Cloud radiative feedback impacts early tropical cyclone (TC) intensification, but limitations in existing diagnostic frameworks make them unsuitable for studying asymmetric or transient radiative heating. We propose a linear Variational Encoder-Decoder (VED) to learn the hidden relationship between radiation and the surface intensification of realistic simulated TCs. Limiting VED model inputs enables using its uncertainty to identify periods when radiation has more importance for intensification. A close examination of the extracted 3D radiative structures suggests that longwave radiative forcing from inner core deep convection and shallow clouds both contribute to intensification, with the deep convection having the most impact overall. We find that deep convection downwind of the shallow clouds is critical to the intensification of Haiyan. Our work demonstrates that machine learning can discover thermodynamic-kinematic relationships without relying on axisymmetric or deterministic assumptions, paving the way towards the objective discovery of processes leading to TC intensification in realistic conditions.