Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subsequently predict TC intensity. Here, we present a prototype model which is trained solely on two inputs: Geo infrared imagery leading up to the synoptic time of interest and intensity estimates up to 6 hours prior to that time. To estimate future TC structure, we compute cloud-top temperature radial profiles from infrared imagery and then simulate the evolution of an ensemble of those profiles over the subsequent 12 hours by applying a Deep Autoregressive Generative Model (PixelSNAIL). To forecast TC intensities at hours 6 and 12, we input operational intensity estimates up to the current time (0 h) and simulated future radial profiles up to +12 h into a ``nowcasting'' convolutional neural network. We limit our inputs to demonstrate the viability of our approach and to enable quantification of value added by the observed and simulated future radial profiles beyond operational intensity estimates alone. Our prototype model achieves a marginally higher error than the National Hurricane Center's official forecasts despite excluding environmental factors, such as vertical wind shear and sea surface temperature. We also demonstrate that it is possible to reasonably predict short-term evolution of TC convective structure via radial profiles from Geo infrared imagery, resulting in interpretable structural forecasts that may be valuable for TC operational guidance.

翻译：由于地球静止卫星（Geo）影像提供了了解热带气旋（TC）行为的高时间分辨率窗口，我们研究了将其应用于短期概率预报TC对流结构进而预测TC强度的可行性。在此，我们提出一个原型模型，该模型仅基于两种输入进行训练：感兴趣天气时间前的地球静止红外影像，以及该时间前6小时内的强度估计。为估算未来TC结构，我们从红外影像中计算云顶温度径向廓线，然后通过应用深度自回归生成模型（PixelSNAIL）模拟这些廓线在未来12小时内的集合演化。为预报6小时和12小时的TC强度，我们将截至当前时间（0小时）的业务强度估计和模拟的未来径向廓线（至+12小时）输入一个“临近预报”卷积神经网络。我们限制输入以证明该方法的可行性，并量化观测和模拟的未来径向廓线相较于仅业务强度估计的附加价值。尽管排除了环境因素（如垂直风切变和海面温度），我们的原型模型误差略高于国家飓风中心官方预报。我们还证明，通过地球静止红外影像的径向廓线合理预测TC对流结构的短期演化是可行的，从而生成可解释的结构性预报，这可能对TC业务指导具有重要价值。