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.

翻译：云辐射反馈影响早期热带气旋（TC）的增强，但现有诊断框架的局限性使其不适用于研究非对称或瞬态辐射加热。我们提出一种线性变分编码器-解码器（VED），用于学习辐射与实际模拟热带气旋地面增强之间的隐藏关系。通过限制VED模型输入，可利用其不确定性识别辐射对增强过程更为重要的时段。对提取的三维辐射结构的详细分析表明，内核心深对流和浅层云的长波辐射强迫均对增强有贡献，其中深对流的整体影响最大。我们发现，浅层云下风方向的深对流对海燕台风增强至关重要。我们的工作表明，机器学习无需依赖轴对称或确定性假设即可发现热力学-运动学关系，为在真实条件下客观发现导致热带气旋增强的过程铺平了道路。