Identifying Three-Dimensional Radiative Patterns Associated with Early Tropical Cyclone Intensification

TL;DR

This paper introduces a machine learning approach using a Variational Encoder-Decoder to identify three-dimensional radiative patterns linked to early tropical cyclone intensification, revealing the importance of deep convection and cloud interactions.

Contribution

The study presents a novel linear VED model that captures asymmetric radiative processes and their role in TC intensification, overcoming limitations of previous diagnostic frameworks.

Findings

Deep convection significantly influences TC intensification.

Downwind deep convection is critical for Haiyan's intensification.

Machine learning uncovers thermodynamic-kinematic relationships without symmetry assumptions.

Abstract

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.